Information processing method, information processing device, and program

Abstract

To provide an information processing method capable of accurately acquiring the depth of an image.SOLUTION: This information processing method acquires an IR image by performing imaging for receiving light emitted from a light source 10 and reflected by a subject, acquires a BW image by imaging the subject by visible light, and detects a dust area on the basis of the IR image and the BW image.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present disclosure relates to a depth acquisition device that acquires the depth of an image. [Background technology]

[0002] Distance measuring devices that measure the distance to a subject have been proposed (see, for example, Patent Document 1). This distance measuring device includes a light source and an imaging unit. The light source irradiates light onto the subject. The imaging unit captures the light reflected by the subject. The distance measuring device then measures the distance to the subject by converting each pixel value of the image obtained by the imaging into the distance to the subject. In other words, the distance measuring device acquires the depth of the image obtained by the imaging unit. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2011-64498 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the distance measuring device of Patent Document 1 has a problem in that it is not possible to accurately obtain the depth of an image.

[0005] Therefore, the present disclosure provides a depth acquisition device that can accurately acquire the depth of an image. [Means for solving the problem]

[0006] In an information processing method according to one embodiment of the present disclosure, an IR image is obtained by capturing light irradiated from a light source and reflected by a subject, a BW image is obtained by capturing an image of the subject using visible light, and a dust area is detected based on the IR image and the BW image.

[0007] An imaging device according to one aspect of the present disclosure includes a light source that irradiates a subject with irradiation light, a solid-state imaging element that takes a first image of the subject and a second image using light reflected from the subject from the irradiation light, a dust detection unit that detects a dust area in which dust is displayed using subject information about the subject output by the solid-state imaging element, and an output unit that generates and outputs output information according to the detected dust area.

[0008] A depth acquisition device according to one aspect of the present disclosure includes a memory and a processor, wherein the processor acquires timing information indicating when a light source irradiates a subject with infrared light, acquires an infrared image obtained by imaging a scene including the subject using infrared light according to the timing indicated by the timing information and stored in the memory, acquires a visible light image of substantially the same scene as the infrared image, obtained by imaging using visible light from substantially the same viewpoint and at substantially the same time as the infrared image and stored in the memory, detects an area in which dust is reflected as a dust area from the infrared image, and estimates the depth of the dust area based on the infrared image, the visible light image, and the dust area.

[0009] These comprehensive or specific aspects may be realized as a system, a method, an integrated circuit, a computer program, or a computer-readable recording medium such as a CD-ROM, or may be realized as any combination of the system, the method, the integrated circuit, the computer program, and the recording medium. The recording medium may also be a non-transitory recording medium. [Effects of the Invention]

[0010] The depth acquisition device of the present disclosure can accurately acquire the depth of an image. Further advantages and effects of one aspect of the present disclosure will become apparent from the specification and drawings. Such advantages and / or effects are provided by some embodiments and features described in the specification and drawings, but not all of them are necessarily provided to obtain one or more identical features. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a block diagram showing a hardware configuration of a depth acquisition device according to an embodiment. [Figure 2] FIG. 2 is a schematic diagram showing a pixel array of the solid-state imaging device according to the embodiment. [Figure 3] FIG. 3 is a timing chart showing the relationship between the light emission timing of the light emitting element of the light source and the exposure timing of the first pixel of the solid-state imaging device in the embodiment. [Figure 4] FIG. 4 is a block diagram illustrating an example of a functional configuration of the depth acquisition device according to the embodiment. [Figure 5] FIG. 5 is a block diagram showing another example of the functional configuration of the depth acquisition device according to the embodiment. [Figure 6] FIG. 6 is a flowchart showing the overall processing operation of the depth acquisition device according to the embodiment. [Figure 7] FIG. 7 is a flowchart showing the overall processing operation by the processor of the depth acquisition device according to the embodiment. [Figure 8] FIG. 8 is a block diagram showing a specific functional configuration of a processor of the depth acquisition device according to the embodiment. [Figure 9A] FIG. 9A is a diagram showing an example of an IR image. [Figure 9B] FIG. 9B is a diagram showing an example of a BW image. [Figure 10] FIG. 10 is a diagram showing an example of a binarized image obtained by binarizing an IR image. [Figure 11] FIG. 11 is a diagram showing an example of a dust candidate region in an IR image. [Figure 12] FIG. 12 is a diagram showing an example of FOE detected in a BW image. [Figure 13] FIG. 13 is a diagram showing an example of a principal axis detected for a dust region. [Figure 14]FIG. 14 is a diagram showing an example of a dust region and a non-dust region. [Figure 15A] FIG. 15A is a diagram showing another example of an IR image. [Figure 15B] FIG. 15B is a diagram showing another example of a BW image. [Figure 16] FIG. 16 is a diagram showing an example of a binarized image obtained by binarizing an IR image. [Figure 17] FIG. 17 is a diagram showing an example of a dust candidate region in an IR image. [Figure 18] FIG. 18 is a diagram showing an example of FOE detected in a BW image. [Figure 19] FIG. 19 is a diagram showing an example of the arrangement of each dust candidate region. [Figure 20] FIG. 20 is a diagram showing a simulation result of the depth acquisition device according to the embodiment. [Figure 21] FIG. 21 is a flowchart showing the overall processing operation of the depth acquisition device shown in FIG. [Figure 22] FIG. 22 is a flowchart showing an example of detailed processing of steps S31 to S34 in FIG. [Figure 23] FIG. 23 is a flowchart showing another example of the detailed processing of steps S31 to S34 in FIG. [Figure 24] FIG. 24 is a flowchart showing an example of processing instead of steps S31 to S34 in FIG. [Figure 25] FIG. 25 is a flowchart showing another example of the detailed processing of steps S31 to S34 in FIG. [Figure 26] FIG. 26 is a block diagram illustrating an example of a functional configuration of a depth acquisition device according to a modification of the embodiment. [Figure 27] FIG. 27 is a block diagram illustrating another example of the functional configuration of the depth acquisition device according to the modification of the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] (Findings that formed the basis of this disclosure) The present inventors have found that the distance measuring device of Patent Document 1, described in the "Background Art" section, has the following problems.

[0013] As described above, the distance measuring device of Patent Document 1 irradiates a subject with light from a light source, captures an image of the illuminated subject, and measures the depth of the image. This depth measurement uses TOF (Time Of Flight). In this type of distance measuring device, imaging is performed under different imaging conditions to improve distance measurement accuracy. That is, the distance measuring device captures an image under predetermined imaging conditions, and then sets imaging conditions different from the predetermined imaging conditions depending on the imaging results. The distance measuring device then captures an image again under the set imaging conditions.

[0014] However, dust particles near the ranging device may appear as noise in the image obtained by capturing the image. As a result, the dust noise cannot be removed from the image in which the dust particles are captured, and the depth cannot be measured accurately. Even if the capturing conditions are changed, it may be difficult to easily suppress the dust particles from appearing. Furthermore, for example, if a ranging device mounted on a vehicle repeatedly captures images under different capturing conditions while the vehicle is traveling, the viewpoint positions for the repeated captures will be different, resulting in different scenes in the multiple images obtained. In other words, it is not possible to capture the same scene repeatedly, and the depth of the image capturing the scene, especially the depth of the area where the dust particles are captured, cannot be accurately estimated.

[0015] In order to solve such problems, a depth acquisition device according to one aspect of the present disclosure includes a memory and a processor, wherein the processor acquires timing information indicating when a light source irradiates a subject with infrared light, acquires an infrared image obtained by imaging a scene including the subject using infrared light according to the timing indicated by the timing information and stored in the memory, acquires a visible light image of substantially the same scene as the infrared image, obtained by imaging using visible light from substantially the same viewpoint and at substantially the same time as the infrared image and stored in the memory, detects an area in which dust is reflected as a dust area from the infrared image, and estimates the depth of the dust area based on the infrared image, the visible light image, and the dust area.

[0016] This allows a dust region to be detected from the infrared image, and the depth of the dust region to be estimated based on not only the infrared image but also the visible light image, thereby enabling the depth of the dust region to be appropriately determined. In other words, the infrared image and the visible light image capture substantially the same scene, and the viewpoint and capture time are also substantially the same. An example of an image of a substantially identical scene captured from substantially the same viewpoint and capture time is an image captured using different pixels of the same image sensor. Such an image is similar to the red, green, and blue channel images of a color image captured using a Bayer array color filter, and the angle of view, viewpoint, and capture time of each image are approximately the same. In other words, in images of a substantially identical scene captured from substantially the same viewpoint and capture time, the position of the subject on the image does not differ by more than two pixels in each captured image. For example, if a point light source having visible light and infrared components exists in the scene and only one pixel is captured with high brightness in the visible light image, the point light source will also be captured in the infrared image closer than two pixels to the pixel corresponding to the pixel position captured in the visible light image. Furthermore, "substantially the same image capture time" means that the difference in image capture time is equal to one frame or less. Therefore, there is a high correlation between the infrared image and the visible light image. However, if there is dust near the camera capturing the infrared image and the visible light image, the irradiated infrared light may be strongly reflected by the dust, and the dust may appear in the infrared image due to this strong reflection. Therefore, even if the dust appears in the infrared image, it is highly likely that it will not appear in the visible light image. Therefore, the missing information in the dust region can be compensated for from the region in the visible light image that corresponds to the dust region (i.e., the corresponding region). As a result, the depth of the dust region can be appropriately obtained without the influence of dust noise.

[0017] For example, in estimating the depth of the dust region, the processor may estimate first depth information indicating the depth at each position in the infrared image, correct the depth at each position in the dust region indicated by the first depth information based on the visible light image to estimate second depth information indicating the corrected depth at each position in the dust region, and further generate third depth information indicating the depth at each position outside the dust region in the infrared image indicated by the first depth information and the depth at each position in the dust region in the infrared image indicated by the second depth information. Note that, in estimating the first depth information, TOF or the like may be applied to the infrared image.

[0018] As a result, the third depth information indicates the depth obtained from the infrared image as the depth outside the dust region of the infrared image, and indicates the depth obtained from the infrared image and corrected based on the visible light image as the depth of the dust region of the infrared image. Therefore, even if the infrared image includes a dust region, the overall depth of the infrared image can be appropriately estimated.

[0019] In addition, when detecting the dust region, the processor may detect a high-brightness region in the infrared image having a brightness equal to or greater than a first threshold as the dust region if the high-brightness region satisfies a first condition.

[0020] Dust regions tend to have high brightness. Therefore, by detecting high-brightness regions in the infrared image that have brightness levels equal to or greater than the first threshold, it is possible to easily narrow down the areas that are likely to contain dust. Furthermore, because high-brightness regions that satisfy the first condition are detected as dust regions, dust regions can be detected with high accuracy by appropriately setting the first condition.

[0021] Furthermore, the first condition may be a condition that the center of gravity of the high-brightness area is located on a straight line or an arc intersecting the center of gravity of each of at least two other high-brightness areas different from the high-brightness area in the infrared image and the FOE (Focus of Expansion) of the infrared image or the visible light image.

[0022] For example, when a camera captures a single dust particle, if multiple exposures are performed to capture one frame of infrared image, the dust particle will appear in the infrared image as a high-brightness area each time an exposure is performed. Therefore, if the camera is mounted on a moving object such as a vehicle, the dust particle is likely to appear in the infrared image as if it is blowing out of the FOE. For example, if the moving object is moving at high speed, the multiple high-brightness areas caused by the dust particle will tend to be located on a straight line intersecting with the FOE. Alternatively, if the camera lens has significant distortion, the multiple high-brightness areas caused by the dust particle will tend to be located on an arc intersecting with the FOE. Therefore, by setting the first condition that the centers of gravity of at least three high-brightness areas are located on the straight line or arc, dust particle areas can be detected with high accuracy.

[0023] It should be noted that the infrared image and the visible light image are captured from substantially the same scene and substantially the same viewpoint, so the FOE of the infrared image and the FOE of the visible light image are substantially the same.

[0024] Alternatively, the first condition may be a condition that a major axis of the high-brightness region or an extension of the major axis intersects with a focus of expansion (FOE) of the infrared image or the visible light image.

[0025] For example, contrary to the above example, if the moving speed of the mobile object carrying the camera is slow, the multiple high-brightness areas will overlap. As a result, these multiple high-brightness areas will appear on the infrared image as a single, elongated, tail-shaped high-brightness area. The main axis of such a high-brightness area caused by dust or an extension of that main axis tends to intersect with the FOE. Therefore, by setting the first condition that the main axis of the high-brightness area or an extension of that main axis intersects with the FOE, dust areas can be detected with high accuracy.

[0026] Furthermore, in detecting the dust region, the processor may detect the high-luminance region as the dust region if the high-luminance region further satisfies a second condition.

[0027] For example, the second condition may be based on the fact that a dust region is observed in an infrared image but not in a visible light image, which allows for more accurate detection of the dust region.

[0028] The second condition may be a condition that the luminance at a position in the visible light image corresponding to the center of gravity of the high luminance area in the infrared image is less than a second threshold value.

[0029] Since dust regions are not observed in BW images, the brightness of the position in the visible light image corresponding to the center of gravity of the dust region tends to be low. Therefore, by setting the second condition that the brightness of the position in the visible light image corresponding to the center of gravity of the high-brightness region in the infrared image be less than the second threshold, dust regions can be detected with higher accuracy.

[0030] The second condition may be a condition that a correlation coefficient between the luminance in a high-luminance region of the infrared image and the luminance in a region of the visible light image corresponding to the high-luminance region is less than a third threshold.

[0031] Because dust regions are observed in infrared images but not in visible light images, there tends to be a low correlation between the brightness of a dust region in an infrared image and the brightness of the corresponding region in the visible light image. Therefore, by setting the second condition that the correlation coefficient between the brightness of a high-brightness region in the infrared image and the brightness of the corresponding region in the visible light image be less than a third threshold, dust regions can be detected with higher accuracy.

[0032] In addition, when estimating the depth of the dust region, the processor may estimate depth information indicating the depth at each position in the infrared image, and correct the depth at each position in the dust region indicated by the depth information by inputting the infrared image, the visible light image, the dust region, and the depth information into a learning model.

[0033] This allows the learning model to be trained in advance so that it can output the correct depth at each position within the dust region in response to inputs of an infrared image, a visible light image, a dust region, and depth information, thereby making it possible to appropriately correct the depth information estimated from the infrared image. In other words, the depth at each position within the dust region indicated by the depth information can be appropriately corrected.

[0034] In addition, a depth acquisition device according to another aspect of the present disclosure includes a memory and a processor, wherein the processor acquires timing information indicating when a light source irradiates a subject with infrared light, acquires an infrared image obtained by infrared light-based imaging of a scene including the subject according to the timing indicated by the timing information and stored in the memory, acquires a visible light image of substantially the same scene as the infrared image, obtained by visible light-based imaging from substantially the same viewpoint and at substantially the same imaging time as the infrared image and stored in the memory, estimates depth information indicating the depth at each position within the infrared image, and inputs the infrared image, the visible light image, and the depth information into a learning model, thereby correcting the depth at each position within a dust region in which dust is depicted in the infrared image, as indicated by the depth information.

[0035] This allows the learning model to be trained in advance so that, in response to input of an infrared image, a visible light image, and depth information, the correct depth at each position within the dust region in the infrared image can be output, thereby enabling the depth information estimated from the infrared image to be appropriately corrected. In other words, the depth at each position within the dust region indicated by the depth information can be appropriately corrected without detecting the dust region.

[0036] According to another aspect of the present disclosure, there is provided a depth acquisition device including a memory and a processor, the processor acquiring an infrared image stored in the memory, the infrared image being obtained by imaging using infrared light, and acquiring a visible light image stored in the memory, the visible light image being obtained by imaging using visible light from substantially the same viewpoint and at the same time as the infrared image, detecting an area in which dust appears as a dust area from the infrared image, and estimating a depth of the dust area based on the visible light image. Furthermore, when the visible light image and the infrared image are each separated into a dust area and other areas, the depth of the dust area is estimated based on the visible light image, and the depth of the other areas is estimated based on the infrared image.

[0037] As a result, similar to the depth acquisition device according to the above aspect of the present disclosure, the depth of the dust region can be appropriately acquired while eliminating the influence of noise that is dust.

[0038] These comprehensive or specific aspects may be realized as a system, a method, an integrated circuit, a computer program, or a computer-readable recording medium such as a CD-ROM, or may be realized as any combination of the system, the method, the integrated circuit, the computer program, or the recording medium. The recording medium may also be a non-transitory recording medium.

[0039] Hereinafter, the embodiments will be specifically described with reference to the drawings.

[0040] The embodiments described below are all comprehensive or specific examples. The numerical values, shapes, materials, components, component placement and connection configurations, steps, and step order shown in the following embodiments are merely examples and are not intended to limit the present disclosure. Furthermore, among the components in the following embodiments, components that are not described in the independent claims that represent the highest concepts are described as optional components.

[0041] In addition, each drawing is a schematic diagram and is not necessarily an exact illustration. In addition, the same components are denoted by the same reference numerals in each drawing.

[0042] (Embodiment) [Hardware configuration] 1 is a block diagram showing the hardware configuration of a depth acquisition device 1 according to an embodiment. The depth acquisition device 1 according to this embodiment has a hardware configuration that can acquire an image based on infrared light (or near-infrared light) and an image based on visible light by capturing images of substantially the same scene from substantially the same viewpoint and at substantially the same capturing time. Note that "substantially the same" means that the images are identical to the extent that the effects of the present disclosure can be achieved.

[0043] As shown in FIG. 1, the depth acquisition device 1 includes a light source 10, a solid-state image sensor 20, a processing circuit 30, a diffuser 50, a lens 60, and a band-pass filter .

[0044] The light source 10 emits illumination light. More specifically, the light source 10 emits illumination light to illuminate the subject at the timing indicated by the light emission signal generated by the processing circuit 30.

[0045] The light source 10 includes, for example, a capacitor, a drive circuit, and a light-emitting element, and emits light by driving the light-emitting element with electrical energy stored in the capacitor. The light-emitting element is realized by, for example, a laser diode, a light-emitting diode, etc. The light source 10 may include one type of light-emitting element, or may include multiple types of light-emitting elements depending on the purpose.

[0046] In the following description, the light-emitting element is, for example, a laser diode that emits near-infrared light, or a light-emitting diode that emits near-infrared light. However, the light emitted by light source 10 does not need to be limited to near-infrared light. The light emitted by light source 10 may be, for example, infrared light (also referred to as infrared light) in a frequency band other than near-infrared light. In the following description of the present embodiment, the light emitted by light source 10 is described as infrared light, but the infrared light may be near-infrared light or infrared light in a frequency band other than near-infrared light.

[0047] The solid-state imaging element 20 captures an image of a subject and outputs an imaging signal indicating the amount of exposure. More specifically, the solid-state imaging element 20 performs exposure at the timing indicated by the exposure signal generated by the processing circuit 30, and outputs an imaging signal indicating the amount of exposure.

[0048] The solid-state imaging element 20 has a pixel array in which first pixels that capture an image using light reflected from a subject and second pixels that capture an image of the subject are arranged in an array. The solid-state imaging element 20 may have logic functions such as a cover glass, an AD converter, etc., as necessary.

[0049] In the following description, the reflected light is assumed to be infrared light, just like the irradiated light. However, the reflected light does not have to be limited to infrared light, as long as it is light that is irradiated light reflected by the subject.

[0050] FIG. 2 is a schematic diagram showing a pixel array 2 included in the solid-state imaging device 20. As shown in FIG.

[0051] As shown in Figure 2, the pixel array 2 is configured in such a way that first pixels 21 (IR pixels) that capture images using light reflected from a subject when irradiated light is reflected, and second pixels 22 (BW pixels) that capture images of the subject are arranged in an array, alternating in columns.

[0052] 2, in the pixel array 2, the second pixels 22 and the first pixels 21 are arranged adjacent to each other in the row direction and arranged in stripes in the row direction, but this is not limited thereto and the pixels may be arranged every several rows (for example, every two rows). That is, the first rows in which the second pixels 22 are arranged adjacent to each other in the row direction and the second rows in which the first pixels 21 are arranged adjacent to each other in the row direction may be arranged alternately every M rows (M is a natural number). Furthermore, the first rows in which the second pixels 22 are arranged adjacent to each other in the row direction and the second rows in which the first pixels 21 are arranged adjacent to each other in the row direction may be arranged at different intervals (the first rows alternate every N rows and the second rows alternate every L rows (N and L are different natural numbers)).

[0053] The first pixel 21 is realized by, for example, an infrared pixel having sensitivity to infrared light, which is reflected light, and the second pixel 22 is realized by, for example, a visible pixel having sensitivity to visible light.

[0054] The infrared light pixel includes, for example, an optical filter (also referred to as an IR filter) that transmits only infrared light, a microlens, a light receiving element as a photoelectric conversion unit, and an accumulation unit that accumulates electric charges generated by the light receiving element. Therefore, an image indicating the luminance of infrared light is expressed by imaging signals output from the multiple infrared light pixels (i.e., first pixels 21) included in the pixel array 2. Hereinafter, this infrared light image will also be referred to as an IR image or an infrared image.

[0055] The visible light pixel includes, for example, an optical filter (also called a BW filter) that transmits only visible light, a microlens, a light receiving element as a photoelectric conversion unit, and an accumulation unit that accumulates the electric charge converted by the light receiving element. Therefore, the visible light pixel, i.e., the second pixel 22, outputs an image signal indicating the luminance and color difference. That is, a color image indicating the luminance and color difference of visible light is expressed by the image signal output from the plurality of second pixels 22 included in the pixel array 2. The optical filter of the visible light pixel may transmit both visible light and infrared light, or may transmit only light of a specific wavelength band, such as red (R), green (G), or blue (B), of visible light.

[0056] Alternatively, the visible light pixels may detect only the luminance of visible light. In this case, the visible light pixels, i.e., the second pixels 22, output imaging signals indicating the luminance. Therefore, imaging signals output from the second pixels 22 included in the pixel array 2 represent a black and white image indicating the luminance of visible light, in other words, a monochrome image. This monochrome image will hereinafter also be referred to as a BW image. The above-mentioned color image and BW image will also be collectively referred to as a visible light image.

[0057] Returning to FIG. 1 again, the description of the depth acquisition device 1 will continue.

[0058] The processing circuit 30 uses the imaging signal output by the solid-state imaging device 20 to calculate subject information relating to the subject.

[0059] The processing circuit 30 is configured, for example, by an arithmetic processing device such as a microcomputer. The microcomputer includes a processor (microprocessor), memory, etc., and generates a light emission signal and an exposure signal by executing a driving program stored in the memory by the processor. The processing circuit 30 may use an FPGA or an ISP, etc., and may be configured by one piece of hardware or by multiple pieces of hardware.

[0060] The processing circuit 30 calculates the distance to the subject by, for example, a TOF distance measurement method using an imaging signal from the first pixel 21 of the solid-state imaging device 20.

[0061] Hereinafter, calculation of the distance to the subject by the TOF distance measurement method performed by the processing circuit 30 will be described with reference to the drawings.

[0062] Figure 3 is a timing diagram showing the relationship between the light emission timing of the light-emitting element of the light source 10 and the exposure timing of the first pixel 21 of the solid-state imaging element 20 when the processing circuit 30 calculates the distance to the subject using the TOF ranging method.

[0063] 3, Tp is the light emission period during which the light emitting element of the light source 10 emits irradiation light, and Td is the delay time from when the light emitting element of the light source 10 emits irradiation light until the reflected light of the irradiation light reflected by the subject returns to the solid-state imaging element 20. The first exposure period has the same timing as the light emission period during which the light source 10 emits irradiation light, and the second exposure period has the timing from the end of the first exposure period until the light emission period Tp has elapsed.

[0064] In Figure 3, q1 indicates the total amount of exposure at the first pixel 21 of the solid-state imaging element 20 due to reflected light during the first exposure period, and q2 indicates the total amount of exposure at the first pixel 21 of the solid-state imaging element 20 due to reflected light during the second exposure period.

[0065] By emitting light from the light-emitting element of the light source 10 and exposing the first pixel 21 of the solid-state imaging element 20 at the timing shown in Figure 3, the distance d to the subject can be expressed by the following (Equation 1), where c is the speed of light.

[0066] d=c×Tp / 2×q2 / (q1+q2) (Formula 1)

[0067] Therefore, by using (Equation 1), the processing circuit 30 can calculate the distance to the subject using the imaging signal from the first pixel 21 of the solid-state imaging device 20.

[0068] Furthermore, the first pixels 21 of the solid-state imaging device 20 may be exposed for only a third exposure period Tp after the first and second exposure periods have ended. The first pixels 21 can detect noise other than reflected light using the exposure amount obtained during this third exposure period Tp. In other words, the processing circuit 30 can more accurately calculate the distance d to the subject by removing noise from the exposure amount q1 of the first exposure period and the exposure amount q2 of the second exposure period in the above (Equation 1).

[0069] Returning to FIG. 1 again, the description of the depth acquisition device 1 will continue.

[0070] The processing circuit 30 may use, for example, an imaging signal from the second pixel 22 of the solid-state imaging device 20 to detect the subject and calculate the distance to the subject.

[0071] That is, the processing circuit 30 may detect the subject and calculate the distance to the subject based on the visible light image captured by the plurality of second pixels 22 of the solid-state imaging device 20. Here, the subject may be detected, for example, by pattern recognition based on edge detection of the subject's singular points to determine its shape, or by processing such as deep learning using a pre-trained learning model. Furthermore, the distance to the subject may be calculated using world coordinate transformation. Of course, the subject may be detected by multimodal learning processing using not only the visible light image but also the luminance and distance information of infrared light captured by the first pixels 21.

[0072] The processing circuit 30 generates a light emission signal indicating the timing of light emission and an exposure signal indicating the timing of exposure, and outputs the generated light emission signal to the light source 10 and the generated exposure signal to the solid-state imaging element 20.

[0073] The processing circuit 30 may, for example, generate and output a light emission signal to cause the light source 10 to emit light at a predetermined cycle, and generate and output an exposure signal to cause the solid-state imaging element 20 to expose at a predetermined cycle, thereby enabling the depth acquisition device 1 to perform continuous imaging at a predetermined frame rate. The processing circuit 30 may also include, for example, a processor (microprocessor), memory, etc., and generate the light emission signal and the exposure signal by the processor executing a driving program stored in the memory.

[0074] The diffuser 50 adjusts the intensity distribution and angle of the irradiated light. In adjusting the intensity distribution, the diffuser 50 makes the intensity distribution of the irradiated light from the light source 10 uniform. In the example shown in FIG. 1, the depth acquisition device 1 includes the diffuser 50, but the diffuser 50 may not be included.

[0075] The lens 60 is an optical lens that focuses light entering from outside the depth acquisition device 1 onto the surface of the pixel array 2 of the solid-state imaging element 20.

[0076] The bandpass filter 70 is an optical filter that transmits infrared light, which is reflected light, and visible light. In the example shown in Fig. 1, the depth acquisition device 1 includes the bandpass filter 70, but the bandpass filter 70 may not be included.

[0077] The depth acquisition device 1 having the above configuration is mounted on transportation equipment for use. For example, the depth acquisition device 1 is mounted on a vehicle that travels on a road surface for use. Note that the transportation equipment on which the depth acquisition device 1 is mounted does not necessarily have to be limited to a vehicle. The depth acquisition device 1 may also be mounted on transportation equipment other than a vehicle, such as a motorcycle, a boat, or an airplane for use.

[0078] [Depth acquisition device overview] The depth acquisition device 1 of this embodiment uses the hardware configuration shown in Fig. 1 to acquire an IR image and a BW image by capturing images of substantially the same scene, from substantially the same viewpoint, and at the same time. The depth acquisition device 1 then corrects the depth at each position in the IR image obtained from the IR image using the BW image. Specifically, if the IR image contains an area in which dust is reflected (hereinafter referred to as a dust area), the depth acquisition device 1 corrects the depth at each position in the dust area obtained from the IR image using an image in the area of ​​the BW image corresponding to the dust area. This makes it possible to properly acquire the depth of the dust area while eliminating the influence of dust noise.

[0079] FIG. 4 is a block diagram showing an example of the functional configuration of the depth acquisition device 1. As shown in FIG.

[0080] The depth acquisition device 1 includes a light source 101, an IR camera 102, a BW camera 103, a depth estimation unit 111, and a dust detection unit 112.

[0081] The light source 101 may be composed of the light source 10 and the diffusion plate 50 shown in FIG.

[0082] 1, a lens 60, and a band-pass filter 70. Such an IR camera 102 captures an IR image by capturing an image of a scene including a subject based on infrared light in accordance with the timing at which a light source 101 irradiates the subject with infrared light.

[0083] The BW camera 103 may be composed of a plurality of second pixels 22 of the solid-state imaging element 20 shown in Fig. 1, a lens 60, and a band-pass filter 70. Such a BW camera 103 captures an image based on visible light of a scene that is substantially the same as the infrared image, from substantially the same viewpoint and at the same time as the infrared image, thereby acquiring a visible light image (specifically, a BW image).

[0084] The depth estimation unit 111 and the dust detection unit 112 may be realized as a function of the processing circuit 30 shown in FIG.

[0085] The dust detection unit 112 detects dust regions from the IR image based on the IR image obtained by imaging with the IR camera 102 and the BW image obtained by imaging with the BW camera 103. In other words, the dust detection unit 112 divides the IR image obtained by imaging into dust regions where dust is reflected and non-dust regions where no dust is reflected.

[0086] If fine particles such as dust are present near the IR camera 102, the dust will appear as large noise in the IR image. In this embodiment, the dust region is a high-luminance region in which dust is captured. For example, if dust is present near the depth acquisition device 1, infrared light is irradiated onto the dust from the light source 101, and the infrared light reflected by the dust is received by the solid-state imaging element 20 while maintaining a high luminance. Therefore, in the IR image, the luminance of each pixel in the region in which dust is captured, i.e., the dust region, is high.

[0087] The depth estimation unit 111 estimates the depth at each position in the IR image including the dust region detected by the dust detection unit 112. Specifically, the depth estimation unit 111 acquires an IR image captured by the IR camera 102 in accordance with the timing at which the light source 101 irradiates the subject with infrared light, and estimates the depth at each position in the IR image based on the IR image. Furthermore, the depth estimation unit 111 corrects the depth at each position estimated in the dust region detected by the dust detection unit 112 based on the BW image. In other words, the depth estimation unit 111 estimates the depth of the dust region based on the IR image, the BW image, and the dust region.

[0088] FIG. 5 is a block diagram showing another example of the functional configuration of the depth acquisition device 1. As shown in FIG.

[0089] The depth acquisition device 1 may include a memory 200 and a processor 110.

[0090] 5, the processor 110 may include not only the depth estimation unit 111 and the dust detection unit 112, but also a light emission timing acquisition unit 113, an IR image acquisition unit 114, and a BW image acquisition unit 115. These components are realized as functions of the processor 110.

[0091] The light emission timing acquisition unit 113 acquires timing information indicating the timing at which the light source 101 irradiates the subject with infrared light. That is, the light emission timing acquisition unit 113 outputs the light emission signal shown in Fig. 1 to the light source 101, thereby acquiring information indicating the output timing as the timing information described above.

[0092] The IR image acquisition unit 114 acquires an IR image that is obtained by capturing an image of a scene including a subject based on infrared light in accordance with the timing indicated by the timing information and that is stored in the memory 200 .

[0093] The BW image acquisition unit 115 acquires a BW image that is captured based on visible light of a scene that is substantially the same as the above-mentioned IR image, and that is obtained by capturing an image from substantially the same viewpoint and at the same time as the IR image, and that is stored in the memory 200.

[0094] As described above, the dust detection unit 112 detects a dust region from the IR image, and the depth estimation unit 111 estimates the depth based on the IR image, the BW image, and the dust region.

[0095] The depth acquisition device 1 in this embodiment may be configured with the processor 110 and the memory 200 without including the light source 101, the IR camera 102, and the BW camera 103.

[0096] FIG. 6 is a flowchart showing the overall processing operation of the depth acquisition device 1.

[0097] (Step S11) First, the light source 101 emits light to irradiate the subject with infrared light.

[0098] (Step S12) Next, the IR camera 102 acquires an IR image. That is, the IR camera 102 captures an image of a scene including an object illuminated with infrared light by the light source 101. As a result, the IR camera 102 acquires an IR image based on the infrared light reflected from the object. Specifically, the IR camera 102 acquires an IR image obtained by the respective timings and exposure amounts of the first exposure period, the second exposure period, and the third exposure period shown in FIG. 3 .

[0099] (Step S13) Next, the BW camera 103 acquires a BW image. That is, the BW camera 103 acquires a BW image corresponding to the IR image acquired in step S12, that is, a BW image of the same scene, the same viewpoint, and the same image capturing time as the IR image.

[0100] (Step S14) Then, the dust detection unit 112 detects a dust region from the IR image acquired in step S12.

[0101] (Step S15) Next, the depth estimation unit 111 estimates the depth of the dust region based on the IR image acquired in step S12, the BW image acquired in step S13, and the dust region detected in step S14.

[0102] FIG. 7 is a flowchart showing the overall processing operation by the processor 110 of the depth acquisition device 1.

[0103] (Step S21) First, light emission timing acquisition unit 113 of processor 110 acquires timing information indicating the timing at which light source 101 irradiates the subject with infrared light.

[0104] (Step S22) Next, the IR image acquisition unit 114 acquires an IR image from the IR camera 102 that captured an image at the timing indicated by the timing information acquired in step S21. For example, the IR image acquisition unit 114 outputs an exposure signal to the IR camera 102 at the timing when the light emission timing acquisition unit 113 outputs the light emission signal shown in FIG. 1 . This causes the IR image acquisition unit 114 to start capturing an image in the IR camera 102, and acquires an IR image obtained by the capturing from the IR camera 102. At this time, the IR image acquisition unit 114 may acquire the IR image from the IR camera 102 via the memory 200, or may acquire the IR image directly from the IR camera 102.

[0105] (Step S23) Next, the BW image acquisition unit 115 acquires a BW image corresponding to the IR image acquired in step S22 from the BW camera 103. At this time, the BW image acquisition unit 115 may acquire the BW image from the BW camera 103 via the memory 200, or may acquire the BW image directly from the BW camera 103.

[0106] (Step S24) Then, the dust detection unit 112 detects a dust region from the IR image.

[0107] (Step S25) Next, the depth estimation unit 111 estimates the depth of the dust region based on the IR image acquired in step S22, the BW image acquired in step S23, and the dust region detected in step S24. This calculates depth information indicating at least the depth of the dust region. Note that at this time, the depth estimation unit 111 may estimate the depth of the entire IR image, not just the dust region, and calculate depth information indicating the estimation result.

[0108] Specifically, the depth estimation unit 111 in this embodiment estimates the depth at each position in the IR image acquired in step S22 from the IR image. Then, the depth estimation unit 111 corrects the depth at each position in the dust region using the BW image. Note that each position may be the position of each of multiple pixels, or may be the position of a block consisting of multiple pixels.

[0109] In the depth acquisition device 1 according to this embodiment, a dust region is detected from an IR image, and the depth of the dust region is estimated based on not only the IR image but also the BW image, making it possible to appropriately acquire the depth of the dust region. In other words, the IR image and the BW image capture substantially the same scene, and the viewpoint and capture time are also substantially the same. Therefore, there is a high correlation between the IR image and the BW image. Even if dust appears in the IR image, there is a high possibility that the dust will not appear in the BW image. Therefore, missing information in the dust region can be compensated for from the region in the BW image that corresponds to the dust region (i.e., the corresponding region). As a result, the depth of the dust region can be appropriately acquired.

[0110] [Specific functional configuration of the depth acquisition device] FIG. 8 is a block diagram showing a specific functional configuration of the processor 110 of the depth acquisition device 1. As shown in FIG.

[0111] The processor 110 includes a first depth estimation unit 111a, a second depth estimation unit 111b, a dust detection unit 112, a high-brightness area detection unit 116, an FOE detection unit 117, and an output unit 118. The first depth estimation unit 111a and the second depth estimation unit 111b correspond to the depth estimation unit 111 shown in Fig. 5. The processor 110 may also include the light emission timing acquisition unit 113, the IR image acquisition unit 114, and the BW image acquisition unit 115 described above.

[0112] The high-luminance region detection unit 116 detects a region in the IR image that has a luminance equal to or greater than a first threshold as a high-luminance region.

[0113] The FOE detection unit 117 detects the FOE (Focus of Expansion) in the BW image. The FOE is also called a vanishing point. When the IR camera 102 moves in a parallel direction and the subject is stationary, it is known that the optical flow, which is the apparent movement on the screen, intersects at one point. This point is the FOE.

[0114] The dust detection unit 112 determines whether or not each of at least one high-brightness region in the IR image is a dust region. The inventors discovered that each dust particle shown in the IR image is formed along a long line or arc intersecting with the FOE, or is arranged along that line or arc, as if it were being blown out of the FOE. When the IR camera 120 is installed on a moving object such as a car, the movement of the dust particle is sufficiently small compared to the movement of the IR camera 120, and it can be assumed that the dust particle is stationary. Therefore, the dust particle appears to be blowing out of the FOE in the IR image.

[0115] Furthermore, in the depth acquisition device 1 according to this embodiment, from the viewpoint of noise reduction, each frame of the IR image and BW image is an image obtained by repeating exposure and shading multiple times. Therefore, the same dust particle appears in multiple locations within a single frame due to exposures at different times within the frame period. Because the dust particle moves as if blowing out of the FOE in the image, the dust regions at each of the multiple locations and the FOE are aligned in a straight line. Furthermore, if the IR camera 120 capturing the same dust particle moves slowly, multiple dust regions resulting from multiple exposures within a single frame period will overlap in the IR image. As a result, a single dust region with a tail-like shape is formed. In this case, the FOE is located in the direction of the tail as viewed from the dust region. The dust detection unit 112 utilizes these properties to detect dust regions.

[0116] That is, in this embodiment, the dust detection unit 112 detects a high-brightness region in an IR image having a brightness equal to or greater than a first threshold as a dust region if the high-brightness region satisfies a first condition. Specifically, the first condition is that the center of gravity of the high-brightness region is located on a line or an arc intersecting the centers of gravity of at least two other high-brightness regions in the IR image that are different from the high-brightness region and the FOE (Focus of Expansion) of the IR image or BW image. Alternatively, the first condition is that the major axis of the high-brightness region or an extension of that major axis intersects with the FOE of the IR image or BW image. Note that this major axis is the axis of the tail if the high-brightness region has a tail shape. This enables dust regions to be detected with high accuracy.

[0117] Furthermore, the dust detection unit 112 may further detect a dust region by utilizing the property that a dust region is observed in an IR image but not in a BW image. That is, the dust detection unit 112 may detect a high-brightness region as a dust region if the high-brightness region further satisfies a second condition. For example, the second condition is a condition in which the luminance at a position in the BW image corresponding to the center of gravity of the high-brightness region in the IR image is less than a second threshold. Alternatively, the second condition is a condition in which the correlation coefficient between the luminance in the high-brightness region in the IR image and the luminance in the region in the BW image corresponding to the high-brightness region is less than a third threshold. Note that the region in the BW image corresponding to the high-brightness region in the IR image is located in the same spatial position as the high-brightness region in the IR image and has the same shape and size as the high-brightness region in the IR image. This allows for even more accurate detection of dust regions.

[0118] The first depth estimation section 111a and the second depth estimation section 111b have the function of the depth estimation section 111 described above.

[0119] The first depth estimation unit 111a estimates the depth at each position in the IR image based on the IR image acquired in accordance with the timing of irradiation of infrared light by the light source 101. The first depth estimation unit 111a outputs information indicating the estimated depth at each position in the IR image as first depth information. In other words, the first depth estimation unit 111a estimates the first depth information indicating the depth at each position in the IR image.

[0120] The second depth estimation unit 111b corrects the first depth information based on the BW image and the dust region in the IR image. As a result, the depth of the dust region, among the depths at each position in the IR image indicated by the first depth information, is corrected. The second depth estimation unit 111b outputs information indicating the corrected depth at each position in the dust region as second depth information. In other words, the second depth estimation unit 111b estimates second depth information indicating the corrected depth at each position in the dust region by correcting the depth at each position in the dust region indicated by the first depth information based on the BW image.

[0121] The output unit 118 replaces the depth at each position within the dust region indicated by the first depth information with the corrected depth at each position within the dust region indicated by the second depth information. This generates third depth information including the depth at each position outside the dust region of the IR image indicated by the first depth information and the corrected depth at each position within the dust region of the IR image indicated by the second depth information. The output unit 118 outputs the third depth information.

[0122] As a result, the third depth information indicates the depth obtained from the IR image as the depth outside the dust region of the IR image, and indicates the depth obtained from the IR image and corrected based on the BW image as the depth of the dust region of the IR image. Therefore, in this embodiment, even if the IR image contains a dust region, the overall depth of the IR image can be appropriately estimated.

[0123] Figure 9A shows an example of an IR image, and Figure 9B shows an example of a BW image.

[0124] As shown in Fig. 9B, the BW image, captured by a BW camera 103 attached to a vehicle, shows a scene of the surroundings of the road on which the vehicle is traveling moving away from the BW camera 103. On this road, dust is being kicked up by the vehicle traveling, for example. When the IR camera 102 captures the same scene as shown in Fig. 9B from the same viewpoint and at the same time as the BW camera 103, the IR image shown in Fig. 9A is acquired.

[0125] As shown in FIG. 9A, the IR image acquired as described above contains high-brightness areas. These areas include areas where dust is captured, i.e., dust areas. For example, the dust areas are confirmed to exist in the center and right portions of the IR image. On the other hand, dust is not captured in the BW image. This is because, when capturing an IR image, infrared light emitted from the light source 101 is diffusely reflected by dust particles near the IR camera 102 and the BW camera 103, but the effect of this diffuse reflection is small when capturing a BW image. Therefore, the dust detection unit 112 detects dust areas by utilizing the property that dust areas are observed in IR images but not in BW images.

[0126] FIG. 10 shows an example of a binarized image obtained by binarizing an IR image.

[0127] The high-brightness region detection unit 116 detects regions having a brightness equal to or greater than a first threshold as high-brightness regions in the IR image shown in Fig. 9A. That is, the high-brightness region detection unit 116 binarizes the brightness at each position (i.e., each pixel) in the IR image. As a result, for example, as shown in Fig. 10, a binarized image consisting of white regions and black regions (hatched regions in Fig. 10) is generated.

[0128] FIG. 11 shows an example of a potential dust region in an IR image.

[0129] The dust detection unit 112 first detects the center of gravity of high-brightness regions, which are white regions, in the binarized image. This center of gravity detection is performed for each of the high-brightness regions in the binarized image. Next, for each of the high-brightness regions in the binarized image, the dust detection unit 112 determines whether the brightness of a position in the BW image corresponding to the center of gravity of that high-brightness region (hereinafter referred to as the center-of-gravity position) is less than a second threshold. That is, the dust detection unit 112 determines whether the high-brightness region satisfies the second condition described above. As a result, the dust detection unit 112 determines, as a candidate dust region, the high-brightness region corresponding to the center-of-gravity position whose brightness is determined to be less than the second threshold. For example, as shown in FIG. 11 , the dust detection unit 112 detects each of five high-brightness regions A to E as a candidate dust region. That is, the IR image or the binarized image is divided into five candidate dust regions A to E and a non-dust region.

[0130] FIG. 12 shows an example of FOE detected for a BW image.

[0131] The FOE detection unit 117 detects the FOE by detecting optical flows from multiple frames of BW images including the BW image shown in FIG. 9B, and finding intersections of the optical flows by robust estimation such as RANSAC (Random Sample Consensus).

[0132] FIG. 13 shows an example of the principal axes detected for the dust candidate regions A to E.

[0133] The dust detection unit 112 detects a principal axis for each of the detected dust candidate regions A to E, as shown in Fig. 11. Specifically, the dust detection unit 112 performs principal component analysis on each pixel of the dust candidate region in the IR image or binarized image, thereby detecting the first principal component axis as the principal axis of the dust candidate region.

[0134] FIG. 14 shows an example of dust and non-dust regions.

[0135] For each of the dust candidate regions A to E in the IR image or the binarized image, the dust detection unit 112 determines whether the main axis or an extension of that main axis of the dust candidate region intersects with the FOE detected by the FOE detection unit 117. That is, the dust detection unit 112 determines whether the dust candidate region satisfies the first condition described above. The dust detection unit 112 then detects dust candidate regions having a main axis or an extension that intersects with the FOE as dust regions, and detects dust candidate regions having a main axis or an extension that does not intersect with the FOE as non-dust regions. For example, as shown in FIG. 14, the extensions of the main axes of dust candidate regions B, C, and E intersect with the FOE, while the main axes and their extensions of dust candidate regions A and D do not intersect with the FOE. Therefore, the dust detection unit 112 detects dust candidate regions B, C, and E as dust regions, and dust candidate regions A and D as non-dust regions.

[0136] Figure 15A shows another example of an IR image, and Figure 15B shows another example of a BW image.

[0137] The BW image shown in Fig. 15B is an image obtained by capturing an image at a different timing from the BW image shown in Fig. 9B. As in the example shown in Fig. 9B, the BW image shown in Fig. 15B is captured by the BW camera 103 attached to a vehicle, and shows a scene of the area around the road on which the vehicle is traveling approaching the BW camera 103. In addition, for example, dust is being stirred up on this road by the vehicle traveling. Therefore, when the IR camera 102 captures the same scene as the scene shown in Fig. 15B from the same viewpoint and at the same time as the BW camera 103, the IR image shown in Fig. 15A is acquired.

[0138] The IR image acquired in this manner contains areas of high brightness, as shown in FIG. 15A. These areas include areas where dust particles are captured, i.e., dust areas. For example, the dust area can be seen in the left part of the IR image. On the other hand, as in the example shown in FIG. 9B, no dust particles are captured in the BW image.

[0139] FIG. 16 shows an example of a binarized image obtained by binarizing an IR image.

[0140] The high-brightness region detection unit 116 detects regions having a brightness equal to or greater than a first threshold as high-brightness regions in the IR image shown in Fig. 15A. That is, the high-brightness region detection unit 116 binarizes the brightness at each position (i.e., each pixel) in the IR image. As a result, for example, as shown in Fig. 16, a binarized image consisting of white regions and black regions (hatched regions in Fig. 16) is generated.

[0141] FIG. 17 shows an example of a candidate dust region in an IR image.

[0142] The dust detection unit 112 first detects the center of gravity of high-brightness regions, which are white regions, in the binary image. This center of gravity detection is performed for each of the high-brightness regions in the binary image. Next, for each of the high-brightness regions in the binary image, the dust detection unit 112 determines whether the luminance of the position in the BW image corresponding to the center of gravity of that high-brightness region (i.e., the center-of-gravity corresponding position) is less than a second threshold. That is, the dust detection unit 112 determines whether the high-brightness region satisfies the second condition described above. As a result, the dust detection unit 112 determines, as a candidate dust region, the high-brightness region corresponding to the center-of-gravity corresponding position whose luminance is determined to be less than the second threshold. For example, as shown in FIG. 17 , the dust detection unit 112 detects, as a candidate dust region, a region group A consisting of multiple high-brightness regions, a high-brightness region B, a region group C consisting of multiple high-brightness regions, and a high-brightness region D. In other words, the IR image or binarized image is divided into dust candidate regions and non-dust regions that are not dust regions. The dust candidate regions consist of the regions included in region group A, region B, the regions included in region group C, and region D.

[0143] FIG. 18 shows an example of FOE detected for a BW image.

[0144] The FOE detection unit 117 detects the FOE by detecting optical flows from multiple frames of BW images including the BW image shown in FIG. 15B, and finding intersections of the optical flows by robust estimation such as RANSAC (Random Sample Consensus).

[0145] FIG. 19 shows an example of the arrangement of each dust candidate region.

[0146] For example, if the vehicle equipped with the IR camera 102 is traveling at a high speed, the same dust particle will be displayed in multiple locations in one frame of the IR image due to exposures at different times within the frame period, as described above. Because the dust particle moves as if blowing out of the FOE, the dust regions at each of the multiple locations and the FOE are arranged on a straight line. Furthermore, if a wide-angle lens or a fisheye lens is used for the IR camera 102, the lens distortion is significant. When the lens distortion is significant, the optical flow, which is the apparent movement on the screen, does not intersect at a single point, and therefore the dust regions and FOE do not exist on a straight line. In this way, when the lens distortion is significant, the dust regions and FOE are arranged on an arc due to the influence of the lens distortion.

[0147] In the example shown in FIG. 19, the dust detection unit 112 determines whether each of the dust candidate regions in region group A, dust candidate region B, each of the dust candidate regions in region group C, and dust candidate region D detected as shown in FIG. 17 is located on an arc together with the FOE. That is, when performing determination on one dust candidate region, the dust detection unit 112 determines whether the center of gravity of the dust candidate region to be determined is located on an arc that intersects with the FOE of the BW image and the centers of gravity of at least two other dust candidate regions different from the dust candidate region to be determined. That is, the dust detection unit 112 determines whether a high-brightness region that is a dust candidate region satisfies the first condition described above. If the center of gravity of the dust candidate region to be determined is located on an arc, the dust detection unit 112 detects the dust candidate region as a dust region. Conversely, if the center of gravity of the dust candidate region to be determined is not located on an arc, the dust detection unit 112 detects the dust candidate region as a non-dust region.

[0148] 19, because each of the multiple dust candidate regions included in area group A and the FOE are arranged on an arc, the dust detection unit 112 detects each of the multiple dust candidate regions included in area group A as a dust region. Similarly, because each of the multiple dust candidate regions included in area group B and the FOE are arranged on an arc, the dust detection unit 112 also detects each of the multiple dust candidate regions included in area group B as a dust region. On the other hand, because each of dust candidate regions B and D is not arranged on an arc that intersects with at least two other dust candidate regions and the FOE, the dust detection unit 112 detects each of dust candidate regions B and D as a non-dust region.

[0149] If lens distortion is significant, the dust detection unit 112 may perform distortion correction processing on the captured BW image and IR image. For example, the dust detection unit 112 may perform distortion correction processing by using a camera calibration method such as R. Tsai, “A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses,” IEEE Journal on Robotics and Automation, Vol. 3, Iss. 4, pp. 323-344, 1987. In this case, the dust detection unit 112 detects the dust candidate region to be determined as a dust region if, in the IR image or its binarized image after distortion correction processing, the dust candidate region to be determined is located on a line intersecting at least two other dust candidate regions and the FOE.

[0150] FIG. 20 shows the simulation results of the depth acquisition device 1.

[0151] The depth acquisition device 1 acquires a BW image shown in (a) of Fig. 20 by capturing an image using the BW camera 103, and further acquires an IR image shown in (b) of Fig. 20 by capturing an image using the IR camera 102. The BW image and IR image are images obtained by capturing an image of the same scene from the same viewpoint and at the same time. In the example shown in (b) of Fig. 20, several dusty areas are present in the IR image.

[0152] The first depth estimation unit 111a generates the first depth information shown in (c) of Fig. 20 by estimating the depth from the IR image. This first depth information is expressed as a first depth image that indicates the depth at each position in the IR image by brightness. In this first depth image, the depth of the dust region is expressed inappropriately.

[0153] The second depth estimation unit 111b corrects the inappropriate depth in the dust region. Then, the output unit 118 generates third depth information indicating the corrected depth of the dust region and the depth of the non-dust region, as shown in (e) of FIG. 20. Like the first depth information, this third depth information is expressed as a third depth image that indicates depth by brightness. Note that the second depth estimation unit 111b may also correct the depth of the non-dust region in the first depth image based on the corresponding region in the BW image.

[0154] In this way, the depth acquisition device 1 according to this embodiment can make the third depth image closer to the correct depth image shown in FIG. 20(d) in the entire image including the dust region.

[0155] [Specific processing flow of the depth acquisition device] FIG. 21 is a flowchart showing the overall processing operation of the depth acquisition device 1 shown in FIG.

[0156] (Step S31) First, the high-brightness region detection unit 116 detects high-brightness regions from the IR image.

[0157] (Step S32) The dust detection unit 112 determines whether a high-brightness region is a dust candidate region, thereby dividing the IR image into dust candidate regions and non-dust regions.

[0158] (Step S33) The FOE detector 117 detects the FOE using the BW image.

[0159] (Step S34) The dust detection unit 112 detects dust regions based on the dust candidate regions and the FOE, thereby dividing the IR image into dust regions and non-dust regions.

[0160] (Step S35) The first depth estimation unit 111a generates first depth information from the IR image using, for example, TOF.

[0161] (Step S36) The second depth estimation unit 111b generates second depth information indicating the depth of the dust region based on the first depth information of the IR image and the BW image.

[0162] (Step S37) The output unit 118 generates third depth information by replacing the depth of the dust region indicated by the first depth information with the depth indicated by the second depth information.

[0163] FIG. 22 is a flowchart showing an example of detailed processing of steps S31 to S34 in FIG.

[0164] (Step S41) First, the high-brightness area detection unit 116 determines whether the brightness at each position in the IR image is equal to or greater than a first threshold. Here, the first threshold may be, for example, about 256 if the IR image is a 12-bit gradation image. Of course, this first threshold may be a value that changes depending on the environmental conditions or the settings of the IR camera 102. For example, when a dark scene such as at night is captured, the brightness of the entire IR image is low, so the first threshold may be a smaller value than when a bright scene is captured in the daytime. Furthermore, when the exposure time of the IR camera 102 is long, the brightness of the entire IR image is high, so the first threshold may be a larger value than when the exposure time is short.

[0165] (Step S42) If it is determined that the brightness at any position is not equal to or greater than the first threshold (No in step S41), the high-brightness area detection unit 116 determines that no dust is captured in the IR image, i.e., the entire IR image is determined to be a dust-free area.

[0166] (Step S43) On the other hand, if it is determined that the luminance at any position is equal to or greater than the first threshold (Yes in step S41), the high-luminance region detection unit 116 divides the IR image into regions. That is, the high-luminance region detection unit 116 divides the IR image into at least one high-luminance region and a region other than the high-luminance region. This region division may be performed using a luminance-based technique such as SuperPixel. Note that the high-luminance region detection unit 116 may perform filtering processing using the size of the region for the region division. For example, if the number of pixels in a high-luminance region is equal to or less than a predetermined number, the high-luminance region detection unit 116 may delete the high-luminance region. That is, even if the high-luminance region detection unit 116 detects a high-luminance region, if the number of pixels in the region is small, the high-luminance region detection unit 116 may reclassify the high-luminance region into a region other than the high-luminance region.

[0167] (Step S44) Next, for each of at least one high-luminance area detected by the area division in step S43, the dust detection unit 112 detects the center of gravity of that high-luminance area. Specifically, the dust detection unit 112 detects the center of gravity of that high-luminance area by calculating the average values ​​of the X-axis coordinate positions and the Y-axis coordinate positions of multiple pixels included in the high-luminance area.

[0168] (Step S45a) The dust detection unit 112 determines whether the brightness of the position in the BW image corresponding to the center of gravity of the high-brightness area (i.e., the center-of-gravity position) is less than the second threshold. That is, the dust detection unit 112 determines whether the high-brightness area satisfies the second condition. If it is determined that the brightness is not less than the second threshold (No in step S45a), the dust detection unit 112 determines the high-brightness area as a non-dust area (step S42). That is, in this case, it is estimated that an object with high light reflectance is captured in both the high-brightness area in the IR image and the area corresponding to the high-brightness area in the BW image. Therefore, in this case, the high-brightness area is determined as a non-dust area. Note that the second threshold may be approximately 20,000 if the BW image is a 12-bit gradation image, for example. Of course, this second threshold may be a value that changes depending on the environmental conditions or the settings of the BW camera 103. For example, when a dark scene such as at night is captured, the brightness of the entire BW image is low, so the second threshold may be a smaller value than when a bright scene is captured in the daytime. Also, when the exposure time of the BW camera 103 is long, the brightness of the entire BW image is high, so the second threshold may be a larger value than when the exposure time is short.

[0169] (Step S46) On the other hand, if the dust detection unit 112 determines that the brightness of the center-of-gravity corresponding position is less than the second threshold (Yes in step S45a), the FOE detection unit 117 detects the FOE based on the BW image.

[0170] (Step S47a) The dust detection unit 112 determines whether the centroids of the three or more dust candidate regions detected in step S44 and the FOE are arranged on a straight line. In other words, the dust detection unit 112 determines whether the dust candidate regions satisfy the first condition described above. Specifically, the dust detection unit 112 fits the centroids of each of the three or more dust candidate regions to a line intersecting the FOE, and determines whether the error (i.e., distance) between the line and each centroid is equal to or less than an allowable value. This determines whether the centroids of each of the three or more dust candidate regions and the FOE are arranged on a straight line. If the error is equal to or less than the allowable value, it is determined that the centroids of those dust candidate regions and the FOE are arranged on a straight line. If the error is not equal to or less than the allowable value, it is determined that the centroids of those dust candidate regions and the FOE are not arranged on a straight line.

[0171] (Step S50) When the dust detection unit 112 determines that the center of gravity of each dust candidate region and the FOE are arranged on a straight line (Yes in step S47a), it determines that the dust candidate region is a dust region.

[0172] (Step S48) On the other hand, if the dust detection unit 112 determines that the centroids of three or more dust candidate regions and the FOE are not arranged on a straight line (No in step S47a), it detects the main axis of each dust candidate region.

[0173] (Step S49) Next, the dust detection unit 112 determines whether the major axis or its extension of each dust candidate region detected in step S48 intersects with the FOE. That is, the dust detection unit 112 determines whether the dust candidate region satisfies a first condition different from the first condition of step S47a. Here, if the dust detection unit 112 determines that the major axis or its extension intersects with the FOE (Yes in step S49), it classifies the dust candidate region having that major axis as a dust region (step S50). On the other hand, if the dust detection unit 112 determines that the major axis or its extension does not intersect with the FOE (No in step S49), it classifies the dust candidate region having that major axis as a dust-free region (step S42).

[0174] This method requires an IR image and a BW image from substantially the same viewpoint. In the depth acquisition device 1 of this embodiment, the filter used for each pixel is set to either an IR filter or a BW filter. That is, as shown in FIG. 2, first pixels 21 having IR filters and second pixels 22 having BW filters are alternately arranged in the column direction. This makes it possible to acquire IR images and BW images from substantially the same viewpoint and at the same time, thereby enabling appropriate identification of dusty areas.

[0175] Fig. 23 is a flowchart showing another example of the detailed processing of steps S31 to S34 in Fig. 21. The flowchart shown in Fig. 23 includes step S47b instead of step S47a among the steps in the flowchart of Fig. 22.

[0176] (Step S47b) For example, if the lens distortion of the IR camera 102 is large, as described above, the dust regions and FOEs are not arranged on a straight line but are arranged on an arc according to the lens distortion.

[0177] Therefore, the dust detection unit 112 may determine whether the centroids and FOEs of the three or more dust candidate regions detected in step S44 are located on a circular arc. Specifically, the dust detection unit 112 obtains an approximation curve between the centroids and FOEs of the three or more dust candidate regions, and determines whether the error (i.e., distance) between the approximation curve and each centroid is equal to or less than an allowable value. This determines whether the centroids and FOEs of the three or more dust candidate regions are located on a circular arc. That is, if the error is equal to or less than the allowable value, it is determined that the centroids and FOEs of the dust candidate regions are located on a circular arc. If the error is not equal to or less than the allowable value, it is determined that the centroids and FOEs of the dust candidate regions are not located on a circular arc. Note that the approximation curve described above is expressed with an order equal to or less than the number of the three or more dust candidate regions to be determined.

[0178] In step S46, the FOE detection unit 117 may detect optical flows from multiple IR images instead of the BW image, find an intersection of the optical flows using robust estimation such as RANSAC, and detect the intersection as the FOE. Alternatively, the FOE detection unit 117 may detect the movement of the IR camera 102 or the BW camera 103, and detect the FOE by calculation using the movement and internal parameters of the IR camera 102 or the BW camera 103.

[0179] Fig. 24 is a flowchart showing an example of processing instead of steps S31 to S34 in Fig. 21. Step S32 in Fig. 21 is omitted in the flowchart shown in Fig. 24. In other words, step S45a in the flowchart in Fig. 22 is omitted in the flowchart shown in Fig. 24.

[0180] 21 to 23, the dust detection unit 112 determines whether a high-brightness region is a dust candidate region by utilizing the property that dust is observed in an IR image but not in a BW image. That is, as shown in step S45a in each of FIGS. 22 and 23, the dust detection unit 112 determines whether a high-brightness region is a dust candidate region based on the brightness of the center-of-gravity corresponding position in the BW image. However, as shown in the flowchart in FIG. 24, the dust detection unit 112 does not have to perform the determination in step S45a. In this case, any high-brightness region obtained by the region division in step S43 is treated as a dust candidate region.

[0181] Fig. 25 is a flowchart showing another example of the detailed processing of steps S31 to S34 in Fig. 21. The flowchart shown in Fig. 25 includes step S45b instead of step S45a among the steps in the flowchart of Fig. 22.

[0182] To determine dust candidate regions, it is possible to more actively utilize the property that dust is observed in IR images but not in BW images. For example, the correlation coefficient between IR images and BW images can be used as this property. As mentioned above, if dust is present, the dust will be captured in the IR image but not in the BW image. In other words, because the BW image captures a more distant object, the IR and BW images, which are viewed from the same viewpoint, differ significantly. Therefore, by using the correlation coefficient between the IR and BW images to determine whether the objects captured are the same or not, it is possible to determine whether each high-brightness region is a dust candidate region.

[0183] (Step S45b) In step S45b, for each of at least one high-brightness region obtained by the region division in step S43, the dust detection unit 112 calculates a correlation coefficient of brightness between that high-brightness region in the IR image and the region in the BW image that corresponds to that high-brightness region (i.e., the corresponding region). The correlation coefficient is found by arranging the brightness of each pixel in the IR image and the BW image into a vector for each region, calculating the dot product, and normalizing it by the number of pixels. In other words, the dust detection unit 112 normalizes the dot product of the vector consisting of the brightness of each pixel in the high-brightness region of the IR image and the vector consisting of the brightness of each pixel in the corresponding region of the BW image. This calculates the correlation coefficient for that high-brightness region.

[0184] The dust detection unit 112 then determines whether the calculated correlation coefficient is less than a third threshold. That is, the dust detection unit 112 determines whether the high-brightness region satisfies the second condition described above. If the correlation coefficient is not less than the third threshold (No in step S45b), the high-brightness region of the IR image and the corresponding region of the BW image may depict the same subject, which may result in a high correlation coefficient. Therefore, in this case, the dust detection unit 112 determines that the high-brightness region is not a dust region (step S42). On the other hand, if the correlation coefficient is less than the third threshold (Yes in step S45b), the high-brightness region of the IR image and the corresponding region of the BW image may depict different subjects, which may result in a low correlation coefficient. Therefore, in this case, the dust detection unit 112 determines that the high-brightness region is a dust candidate region (step S50).

[0185] Furthermore, the distinction between dust and non-dust regions can be achieved using a learning process. Deep learning, for example, can be used for the learning process. In this case, for learning, an IR image, a BW image, and a ground truth image in which the IR image is divided into dust and non-dust regions are prepared in advance. Next, the IR image and BW image are provided as inputs to a learning model. The learning model is then trained so that the output from the learning model in response to the input matches the ground truth image. For example, the learning model is a neural network. The output from the learning model is an image in which each pixel displays a value of "0" or "1," where "0" indicates that the pixel belongs to a non-dust region and "1" indicates that the pixel belongs to a dust region.

[0186] The dust detection unit 112 distinguishes between dust and non-dust regions by using the learning model that has been trained in advance in this way. That is, the dust detection unit 112 inputs an IR image and a BW image to the learning model. The dust detection unit 112 then distinguishes as non-dust regions regions those regions that contain pixels corresponding to the numerical value "0" output from the learning model. Furthermore, the dust detection unit 112 distinguishes as dust regions regions those regions that contain pixels corresponding to the numerical value "1" output from the learning model.

[0187] Through the above process, the dust detection unit 112 divides the captured IR image into a dust region where dust is reflected and a non-dust region where no dust is reflected.

[0188] <Depth correction processing> The second depth estimation unit 111b generates second depth information using the BW image, the first depth information, and the dust region (that is, the result of the above-mentioned region discrimination).

[0189] As described above, dust noise is a phenomenon caused by diffuse reflection of infrared light emitted from the light source 101. Therefore, dust that appears as noise in the IR image often does not appear in the BW image. Therefore, by correcting the first depth information for only the dust region using the BW image instead of the first depth information obtained from the IR image, it is possible to obtain second depth information that is not affected by the dust that appears in the IR image.

[0190] A guided filter, which is a type of image correction filter, may be used to acquire the second depth information. Guided filters are disclosed in a non-patent document (Kaiming He, Jian Sun and Xiaoou Tang, “Guided Image Filtering”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, Iss. 6, pp. 1397-1409, 2013.). A guided filter is a filter that corrects a target image by utilizing the correlation between the target image and a reference image. In the guided filter, it is assumed that the reference image I and the target image p are expressed by parameters a and b as shown in the following (Equation 2).

[0191]

number

[0192] Here, q is the output image obtained by correcting the target image p, i is the number of each pixel, and ωk is the peripheral area of ​​pixel k. Furthermore, parameters a and b are expressed by the following (Equation 3).

[0193]

number

[0194] where ε is a regularization parameter, μ and σ are the mean and variance within the block of the reference image, and are calculated by the following (Equation 4).

[0195]

number

[0196] However, in order to suppress the noise contained in the obtained parameters a and b, averaged parameters are used and the output is calculated as shown in the following (Equation 5).

[0197]

number

[0198] In this embodiment, the second depth estimation unit 111b corrects the first depth information (or first depth image), which is the target image, by providing a BW image as a reference image. This generates or acquires second depth information. To generate such second depth information, an IR image and a BW image having substantially the same viewpoint are required. In the depth acquisition device 1 of this embodiment, the filter used for each pixel is set to either an IR filter or a BW filter. That is, as shown in FIG. 2, first pixels 21 having IR filters and second pixels 22 having BW filters are alternately arranged in the column direction. This allows an IR image and a BW image having substantially the same viewpoint to be acquired, thereby enabling appropriate second depth information to be acquired.

[0199] Of course, the second depth estimation unit 111b may use processing other than the guided filter. For example, the second depth estimation unit 111b may use processing such as a bilateral filter (Non-Patent Document: C. Tomasi, R. Manduchi, “Bilateral filtering for gray and color images,” IEEE International Conference on Computer Vision (ICCV), pp. 839-846, 1998) or Mutual-Structure for Joint Filtering (Non-Patent Document: Xiaoyong Shen, Chao Zhou, Li Xu, and Jiaya Jia, “Mutual-Structure for Joint Filtering,” IEEE International Conference on Computer Vision (ICCV), 2015).

[0200] As described above, in this embodiment, the first depth information is used for areas determined not to contain dust (i.e., non-dust areas), and the second depth information is used for areas that contain dust (i.e., dust areas). This makes it possible to obtain more accurate depth information even when dust is contained in the IR image.

[0201] (Variation) In the above embodiment, a filter such as a guided filter is used to generate the second depth information, but the second depth information may be generated using a learning model.

[0202] For example, deep learning, a learning process, may be used, as described in a non-patent document (Shuran Song, Fisher Yu, Andy Zeng, Angel X. Chang, Manolis Savva, and Thomas Funkhouser, “Semantic Scene Completion from a Single Depth Image,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 190-198, 2017). That is, a learning model may be trained to output second depth information when a BW image and first depth information are input. The non-patent document proposes a network that, when depth information including a missing region and a color image are input, interpolates the missing region of the depth information. The second depth estimation unit 111b in this embodiment provides an IR image, a BW image, and the first depth information to a network (i.e., a learning model) similar to that described in the non-patent document, and further provides the dust region detected by the dust detection unit 112 as a mask image of the missing region. This allows more accurate second depth information to be acquired from the network.

[0203] FIG. 26 is a block diagram showing an example of the functional configuration of the depth acquisition device 1 in this modified example.

[0204] The depth acquisition device 1 in this modification includes the components shown in FIG. 8, and further includes a learning model 104 formed of, for example, a neural network.

[0205] The second depth estimation unit 111b inputs three types of data, namely, an IR image, a BW image, and first depth information, into the learning model 104, and generates second depth information using the dust area as a mask area to be corrected.

[0206] In training the learning model 104, a correct depth image is prepared in advance in addition to an IR image, a BW image, and the first depth information. Next, the IR image, the BW image, the first depth information, and a mask image specifying the dust region are provided as inputs to the learning model 104. The learning model 104 is then trained so that the output from the learning model 104 in response to the input matches the correct depth image. Note that the mask image is provided randomly during training. The second depth estimation unit 111b uses the learning model 104 that has been trained in this manner in advance. In other words, the second depth estimation unit 111b can acquire the second depth information output from the learning model 104 by inputting the IR image, the BW image, the first depth information, and the mask image specifying the dust region to the learning model 104.

[0207] In this modification, the second depth estimation unit 111b estimates depth information indicating the depth at each position in the IR image, and corrects the depth at each position in the dust region indicated by the depth information by inputting the IR image, BW image, dust region, and depth information into the learning model. Therefore, if the learning model is trained in advance so that it outputs the correct depth at each position in the dust region in response to the input of the IR image, BW image, dust region, and depth information, the depth information estimated from the IR image can be appropriately corrected. In other words, the depth at each position in the dust region indicated by the depth information can be appropriately corrected.

[0208] As described above, the second depth estimation unit 111b may use deep learning. In this case, there is no need to output the dust region, and the second depth information may be generated directly by deep learning.

[0209] FIG. 27 is a block diagram showing another example of the functional configuration of the depth acquisition device 1 in this modified example.

[0210] The depth acquisition device 1 in this modification does not include the dust detection unit 112, the high brightness area detection unit 116, and the FOE detection unit 117 among the components shown in FIG. 26, but includes other components.

[0211] In training the training model 104, similar to the example shown in FIG. 26, a correct depth image is prepared in advance in addition to an IR image, a BW image, and first depth information. Next, the IR image, the BW image, and the first depth information are provided as inputs to the training model 104. The training model 104 is trained so that the output from the training model 104 in response to the input matches the correct depth image. As the training model 104, a VGG-16 network with added skip connections may be used, as described in non-patent document (Caner Hazirbas, Laura Leal-Taixe, and Daniel Cremers C. Hazirbas, “Deep Depth From Focus”, In ArXiv preprint arXiv, 1704.01085, 2017.). The number of channels of the training model 104 is changed so that the IR image, the BW image, and the first depth information are provided as inputs to the training model 104. By using the learning model 104 that has been trained in this manner, the second depth estimation unit 111b can easily obtain the second depth information from the learning model 104 by inputting the IR image, the BW image, and the first depth information into the learning model 104.

[0212] That is, the depth acquisition device 1 shown in FIG. 27 includes a memory and a processor 110. Note that the memory is not shown in FIG. 27, but may be included in the depth acquisition device 1 as shown in FIG. 5. The processor 110 acquires timing information indicating the timing at which the light source 101 irradiates the subject with infrared light. Next, the processor 110 acquires an IR image obtained by capturing an image of a scene including the subject using infrared light according to the timing indicated by the timing information and stored in memory. Next, the processor 110 acquires a BW image obtained by capturing an image of substantially the same scene as the IR image using visible light, from substantially the same viewpoint and at substantially the same time as the IR image, and stored in memory. The first depth estimation unit 111a of the processor 110 then estimates depth information indicating the depth at each position within the IR image. The second depth estimation unit 111b inputs the IR image, the BW image, and the depth information into the learning model 104, thereby correcting the depth at each position within the dust region of the IR image indicated by the depth information.

[0213] Therefore, if the learning model 104 is trained in advance so that it outputs the correct depth at each position within the dust region of the IR image in response to the input of an IR image, a BW image, and depth information, the depth information estimated from the IR image can be appropriately corrected. In other words, the depth at each position within the dust region indicated by the depth information can be appropriately corrected without detecting the dust region.

[0214] As described above, in the depth acquisition device 1 of this embodiment and its variations, even if there is a dust area in the IR image, the appropriate depth at each position within the dust area can be acquired by using an image of the corresponding area in the BW image.

[0215] In each of the above embodiments, each component may be configured with dedicated hardware, or may be realized by executing a software program suitable for each component. Each component may be realized by a program execution unit such as a CPU or processor reading and executing a software program recorded on a recording medium such as a hard disk or semiconductor memory. Here, the software that realizes the depth acquisition device of the above embodiment and modified example causes a computer to execute each step included in the flowcharts of any of Figures 6, 7, and 21 to 25.

[0216] While the depth acquisition device according to one or more aspects has been described above based on the embodiment and its modifications, the present disclosure is not limited to the embodiment and its modifications. As long as it does not deviate from the spirit of the present disclosure, various modifications conceivable by a person skilled in the art to the embodiment and its modifications, and configurations constructed by combining the components of the embodiment and its modifications may also be included within the scope of the present disclosure.

[0217] For example, in the above embodiment and its variations, the dust detection unit 112 detects dust regions. However, it may also detect regions other than dust regions, as long as the region captures objects as noise, similar to dust. For example, the dust detection unit 112 may detect rain regions where raindrops are captured or snow regions where snowflakes are captured. In a light rain environment, if the size of the raindrops is small enough relative to the resolution of the BW image captured by the BW camera 103 or the BW image acquisition unit 115, the raindrops will not appear in the BW image. However, in the IR image captured by the IR camera 102 or the IR image acquisition unit 114, infrared light from the light source 101 is reflected by the raindrops and observed as high brightness. As a result, the first depth information or first depth image generated by the first depth estimation unit 111a will have an inappropriate depth for the rain region. Similarly, even in a snowy environment, if the size of snow particles is sufficiently small relative to the resolution of the BW image acquired by the BW camera 103 or the BW image acquisition unit 115, the snow particles will not appear in the BW image. However, in the IR image acquired by the IR camera 102 or the IR image acquisition unit 114, infrared light from the light source 101 is reflected by the snow particles and observed as high brightness. As a result, the first depth information or first depth image generated by the first depth estimation unit 111a will have an inappropriate depth of the snow region. Therefore, the dust detection unit 112 detects rain regions or snow regions using a method similar to that used to detect dust regions. As a result, the second depth estimation unit 111b generates second depth information using the BW image, the first depth information, and the region (i.e., the rain region or the snow region) detected by the dust detection unit 112. This makes it possible to acquire second depth information that is not affected by rain or snow. Note that, while dust in this disclosure includes solid particles such as dust, it is not limited to solid particles and may also include liquid particles. For example, dust in the present disclosure may include at least one of dust particles, raindrops, and snow particles.

[0218] In addition, in this disclosure, all or part of the units, devices, or all or part of the functional blocks in the block diagrams shown in Figures 1, 4, 5, 8, 26, and 27 may be implemented by one or more electronic circuits, including a semiconductor device, a semiconductor integrated circuit (IC), or an LSI (large scale integration). The LSI or IC may be integrated into a single chip or may be configured by combining multiple chips. For example, functional blocks other than memory elements may be integrated into a single chip. Although the terms LSI and IC are used here, the term may be changed depending on the degree of integration, and may be called a system LSI, a VLSI (very large scale integration), or an ULSI (ultra large scale integration). A field programmable gate array (FPGA), which is programmable after LSI fabrication, or a reconfigurable logic device, which can reconfigure the connections within the LSI or set up circuit partitions within the LSI, may also be used for the same purpose.

[0219] Furthermore, all or part of the functions or operations of a unit, device, or part of a device can be implemented by software processing. In this case, the software is recorded on one or more non-transitory recording media such as ROMs, optical disks, hard disk drives, etc., and when the software is executed by a processor, the software causes the processor and peripheral devices to perform specific functions within the software. A system or device may include one or more non-transitory recording media on which the software is recorded, a processor, and necessary hardware devices, such as interfaces. [Industrial Applicability]

[0220] The present disclosure is applicable to a depth acquisition device that acquires depth from an image obtained by capturing, and can be used as, for example, an in-vehicle device. [Explanation of symbols]

[0221] 1 Depth acquisition device 10, 101 light source 20 Solid-state imaging device 21 1st pixel (IR) 22 2nd pixel (BW) 30 Processing circuit 50 Diffuser 60 lenses 70 Bandpass Filter 102 IR camera 103 BW Camera 104 Learning Model 110 processors 111 Depth estimation unit 111a First depth estimation unit 111b Second depth estimation unit 112 Dust detection unit 113 Light emission timing acquisition unit 114 IR image acquisition unit 115 BW image acquisition unit 116 High-brightness area detection unit 117 FOE detection unit 118 Output section 200 memory

Claims

1. An infrared image is obtained by capturing light that is irradiated from a light source and reflected by the subject. capturing an image of the subject using visible light to obtain a visible light image; Detecting a dust region based on the infrared image and the visible light image; the detection of the dust region includes detecting whether a brightness of a region in the infrared image corresponding to a region in the visible light image is equal to or greater than a threshold; Information processing methods.

2. In detecting the dust region, if a brightness of a region in the infrared image corresponding to a region in the visible light image is equal to or greater than a threshold, the region in the infrared image is detected as the dust region. The information processing method according to claim 1 .

3. the detection of the dusty region includes detecting the dusty region based on the infrared image and the visible light image generated by capturing the same scene as the infrared image; The information processing method according to claim 1 .

4. The dust area is detected by capturing the infrared image and the image from the same viewpoint as the infrared image. and detecting the dust region based on the visible light image generated by the above method. The information processing method according to claim 1 .

5. In detecting the dusty region, the dusty region is detected based on the infrared image and the visible light image generated by capturing the infrared image at the same time as the infrared image. The information processing method according to claim 1 .

6. The infrared image is acquired using ToF (Time of Flight). The information processing method according to claim 1 .

7. An infrared image is obtained by capturing light that is irradiated from a light source and reflected by a subject, capturing an image of the subject using visible light to obtain a visible light image; Detecting a dust region based on the infrared image and the visible light image; In detecting the dusty area, based on the infrared image and the visible light image, if a high-brightness region in the infrared image having a brightness equal to or greater than a first threshold satisfies a first condition, the high-brightness region is detected as the dust region; Information processing methods.

8. The first condition is The condition is that the center of gravity of the high-brightness area is located on a line or an arc intersecting the center of gravity of at least two other high-brightness areas different from the high-brightness area in the infrared image and the FOE (Focus of Expansion) of the infrared image or the visible light image. The information processing method according to claim 7.

9. The first condition is a condition in which a main axis of the high-brightness region or an extension of the main axis intersects with a focus of expansion (FOE) of the infrared image or the visible light image; The information processing method according to claim 7.

10. In detecting the dusty area, If the high-brightness area further satisfies a second condition, the high-brightness area is detected as the dust area. The information processing method according to any one of claims 7 to 9.

11. The second condition is: a condition that the luminance at a position in the visible light image corresponding to the center of gravity of the high luminance region in the infrared image is less than a second threshold value; The information processing method according to claim 10.

12. The second condition is: a condition that a correlation coefficient between a luminance in a high-luminance region of the infrared image and a luminance in a region of the visible light image corresponding to the high-luminance region is less than a third threshold value; The information processing method according to claim 10.

13. An infrared image is obtained by capturing light that is irradiated from a light source and reflected by a subject, capturing an image of the subject using visible light to obtain a visible light image; Detecting a dust region based on the infrared image and the visible light image; the infrared image and the visible light image are acquired by a solid-state imaging device; the solid-state imaging device includes a plurality of first pixels used to capture the infrared image and a plurality of second pixels different from the plurality of first pixels used to capture the visible light image; Information processing methods.

14. when the infrared images are captured continuously at a predetermined frame rate, a difference between an imaging time of the visible light image and an imaging time of the infrared image is equal to or less than a time period for one frame at the frame rate; The information processing method according to claim 1 .

15. a processor; a memory; The processor uses the memory to: Acquire an infrared image generated by capturing light irradiated from a light source and reflected by a subject; acquiring a visible light image generated by capturing an image of the subject using visible light; Detecting a dust region based on the infrared image and the visible light image; the detection of the dust region includes detecting whether a brightness of a region in the infrared image corresponding to a region in the visible light image is equal to or greater than a threshold; Information processing device.

16. Acquire an infrared image generated by capturing light irradiated from a light source and reflected by a subject; acquiring a visible light image generated by capturing an image of the subject using visible light; detecting a dust region based on the infrared image and the visible light image; Let the computer do that, the detection of the dust region includes detecting whether a brightness of a region in the infrared image corresponding to a region in the visible light image is equal to or greater than a threshold; program.

Images