Imaging device, vehicle lighting device, vehicle, and imaging method

Abstract

An illumination device 110 in an imaging device 100 sequentially emits reference light having a spatially random intensity distribution M (M ≥ 2) times. A computation processing device 130 divides the M emissions into k (k ≥ 2) units, generates intermediate images by carrying out correlation calculation on the divided units, and generates a restored image G(x, y) by combining the k intermediate images obtained from the k units.

Description

[Technical field]

[0001] The present invention relates to an imaging device that utilizes ghost imaging. [Background technology]

[0002] For autonomous driving and automatic control of headlamp distribution, an object identification system is used to sense the position and type of objects around the vehicle. The object identification system includes a sensor and a processing unit that analyzes the sensor output. The sensor is selected from cameras, LiDAR (Light Detection and Ranging, Laser Imaging Detection and Ranging), millimeter wave radar, ultrasonic sonar, etc., taking into consideration the application, required accuracy, and cost.

[0003] One type of imaging device (sensor) that is known is one that utilizes the principle of ghost imaging. Ghost imaging irradiates an object with a reference light intensity distribution (pattern) that is randomly switched, and measures the optical detection intensity of the reflected light for each pattern. The optical detection intensity is the integral value of the energy or intensity over a certain plane, not an intensity distribution. Then, a correlation calculation is performed between the corresponding pattern and the optical detection intensity, and a restored image of the object is reconstructed. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 6412673 Summary of the Invention [Problem to be solved by the invention]

[0005] As a result of examining imaging devices, the present inventors have come to recognize the following problem. For correlation in ghost imaging, the correlation function of Equation (1) is used. I ris the rth (r=1,2…,M) intensity distribution, and b r is the value of the detected intensity obtained when the reference light having the rth intensity distribution is irradiated.

number

[0006] FIG. 1 is a time chart showing sensing of one frame of an imaging device. As can be seen from equation (1), the correlation calculation uses M detection intensities b1 to b2 obtained by irradiating the reference light M times. M The average value of is necessary.

[0007] Therefore, after M irradiations are completed, the average and start correlation calculation. Here, a photodetector is used to measure the light detection intensity. In addition to the reflected light from the object (i.e., the signal component), disturbance light (i.e., the noise component) is incident on the photodetector. If the intensity of the disturbance light is constant during the irradiation period T in which M irradiations are performed, the effect of the noise is canceled. However, if the intensity of the disturbance light fluctuates during the irradiation period T, the effect of the noise is not canceled, and the image quality of the reconstructed image deteriorates. For example, when the imaging device is mounted on a moving object such as an automobile, this problem becomes prominent when the distance between the noise light source and the moving object changes.

[0008] The present invention has been made in light of the above-mentioned circumstances, and one exemplary purpose of an embodiment of the present invention is to provide an imaging device having improved resistance to time-varying noise. [Means for solving the problem]

[0009] One aspect of the present invention relates to an imaging device. The imaging device includes an illumination device that sequentially switches and irradiates M (M≧2) reference light beams having a spatially random intensity distribution, a photodetector that measures reflected light from an object, and a processor that performs correlation calculation between the detected intensity based on the output of the photodetector and the intensity distribution of the reference light beams to reconstruct a restored image of the object. The processor divides the M reference light beams into k (k≧2) units, performs correlation calculation for each divided unit to generate an intermediate image, and synthesizes the k intermediate images obtained for the k units to generate a restored image.

[0010] Another embodiment of the present invention is also an imaging device. This imaging device includes an illumination device that sequentially switches between and irradiates M reference light beams having spatially random intensity distributions, a photodetector that measures reflected light from an object, and a processor that performs correlation calculation between the detection intensity based on the output of the photodetector and the intensity distribution of the reference light beams to reconstruct a restored image of the object. The processor performs correlation calculation every time n (n≧2) reference light beams are irradiated to generate an intermediate image, and synthesizes the latest k (k≧2) intermediate images to generate a restored image. Effect of the Invention

[0011] According to an aspect of the present invention, it is possible to improve the immunity of an imaging device to time-varying noise. [Brief description of the drawings]

[0012] [Figure 1] 4 is a time chart showing sensing of one frame by the imaging device. [Diagram 2] FIG. 1 is a diagram showing an imaging device according to a first embodiment. [Diagram 3] 4 is a time chart illustrating the operation of the imaging apparatus. [Figure 4] FIG. 4(a) is a diagram showing the effect of noise on collective restoration, and FIG. 4(b) is a diagram showing the effect of noise on divided restoration. [Diagram 5] FIG. 5(a) shows a target image, and FIG. 5(b) shows images obtained by collective restoration and divided restoration when no noise is present. [Figure 6] FIG. 13 is a diagram showing a restored image G(x, y) obtained by collective restoration and divided restoration when linear noise is present. [Figure 7] FIG. 11 is a diagram illustrating an imaging method according to the second embodiment. [Figure 8] FIG. 1 is a block diagram of an object identification system. [Figure 9] FIG. 1 is a block diagram of a vehicle equipped with an object identification system. [Figure 10] 1 is a block diagram showing a vehicle lamp equipped with an object detection system; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] (Outline of the embodiment) In this specification, "intensity distribution is random" does not mean that it is completely random, but it is sufficient that it is random to the extent that an image can be reconstructed in ghost imaging. Therefore, in this specification, "random" can include a certain degree of regularity. In addition, "random" does not require that it is unpredictable, but may be predictable and reproducible.

[0014] One embodiment disclosed in this specification relates to an imaging device. The imaging device includes an illumination device that sequentially switches and irradiates M (M≧2) reference light beams having a spatially random intensity distribution, a photodetector that measures reflected light from an object, and a processor that performs correlation calculation between the detection intensity based on the output of the photodetector and the intensity distribution of the reference light beams to reconstruct a restored image of the object. The processor divides the M reference light beams into a plurality of k (k≧2) units, performs correlation calculation for each divided unit to generate an intermediate image, and synthesizes the k intermediate images obtained for the k units to generate a restored image.

[0015] By dividing the correlation calculation into units, the amount of noise change per correlation calculation can be reduced, improving the restoration accuracy and image quality. In addition, the correlation calculation can be started without waiting for the completion of irradiation of M reference beams, which shortens the time required to generate a restored image.

[0016] The number of reference beams included in each unit may be M / k, which can simplify signal processing.

[0017] The number of units k may be dynamically controlled, and the image quality can be improved by selecting an optimal number of units depending on the noise fluctuation speed and noise waveform.

[0018] The number of irradiations per unit may be different for each unit.

[0019] One embodiment disclosed in this specification also relates to an imaging device. The imaging device includes an illumination device that sequentially switches between M reference light beams having spatially random intensity distributions and irradiates the object, a photodetector that measures reflected light from the object, and a processor that performs correlation calculation between the detection intensity based on the output of the photodetector and the intensity distribution of the reference light beams and reconstructs a restored image of the object. The processor performs correlation calculation every time n (n≧2) reference light beams are irradiated to generate an intermediate image, and synthesizes the latest k (k≧2) intermediate images to generate a restored image.

[0020] By dividing the correlation calculation into units, the amount of noise change per correlation calculation can be reduced, improving the restoration accuracy and image quality. In addition, the update speed (frame rate) of the restored image can be increased.

[0021] (Embodiment) The present invention will be described below based on preferred embodiments with reference to the drawings. The same or equivalent components, parts, and processes shown in each drawing are given the same reference numerals, and duplicated descriptions are omitted as appropriate. In addition, the embodiments are illustrative rather than limiting the invention, and all features and combinations thereof described in the embodiments are not necessarily essential to the invention.

[0022] (Embodiment 1) 2 is a diagram showing an imaging device 100 according to the first embodiment. The imaging device 100 is a correlation function image sensor that uses the principle of ghost imaging, and includes an illumination device 110, a photodetector 120, and a processor 130. The imaging device 100 is also called a quantum radar camera.

[0023] The illumination device 110 is a pseudo-thermal light source that generates a reference light S1 having a spatial intensity distribution I(x,y) that can be considered substantially random, and irradiates the object OBJ. The reference light S1 is irradiated sequentially a number M of times while randomly changing its intensity distribution. The number of irradiations M is a number that can restore the original image in a conventional imaging method (batch restoration described later).

[0024] The illumination apparatus 110 includes a light source 112, a patterning device 114, and a pattern generator 132. The light source 112 generates light S0 having a uniform intensity distribution. The light source 112 may be a laser, a light-emitting diode, or the like. The wavelength and spectrum of the reference light S1 are not particularly limited, and may be white light having multiple or continuous spectra, or monochromatic light including a predetermined wavelength. The wavelength of the reference light S1 may be infrared or ultraviolet.

[0025] Patterning device 114 has a plurality of pixels arranged in a matrix, and is configured to be able to spatially modulate light intensity distribution I based on a combination of on and off states of the plurality of pixels. In this specification, a pixel in an on state is called an on pixel, and a pixel in an off state is called an off pixel. In the following description, for ease of understanding, each pixel will take only two values ​​(1, 0), on and off, but this is not limited thereto, and pixels may take intermediate gradations.

[0026] A reflective DMD (Digital Micromirror Device) or a transmissive liquid crystal device can be used as the patterning device 114. A pattern signal PTN (image data) generated by a pattern generator 132 is provided to the patterning device 114.

[0027] The pattern generator 132 generates the intensity distribution I r The pattern signal PTN that specifies r and generates a pattern signal PTN over time. r Switch between (r=1,2,…M).

[0028] The photodetector 120 measures the reflected light from the object OBJ and generates a detection signal D r The detection signal D is output. r is the intensity distribution I r is a spatial integral value of the light energy (or intensity) incident on the photodetector 120 when the object OBJ is irradiated with a reference light having a value of . Therefore, a single-pixel photodetector can be used as the photodetector 120. The photodetector 120 detects a plurality of M intensity distributions I1 to I M A plurality of detection signals D1 to D M will be output.

[0029] The arithmetic processing device 130 includes a pattern generator 132 and a reconstruction processing unit 134. The reconstruction processing unit 134 generates a plurality of intensity distributions (also called random patterns) I1 to I M and multiple detection intensities b1 to b M Then, the correlation calculation is performed to reconstruct a restored image G(x, y) of the object OBJ.

[0030] Detection strength b1~b M are detection signals D1 to D M The relationship between the detection intensity and the detection signal may be determined taking into consideration the type and method of the photodetector 120.

[0031] A certain intensity distribution I r Assume that the reference light S1 is irradiated for a certain irradiation period. Also, the detection signal D r represents the amount of light received at a certain time (or a very short time), i.e., the instantaneous value. In this case, the detection signal D r is sampled multiple times, and the detection intensity b r The detection signal D r Alternatively, some of the sampled values ​​may be selected and the integral, average, or maximum value of the selected sampled values ​​may be used. The selection of multiple sampled values ​​may involve, for example, extracting values ​​from the xth to yth values ​​in the order counting from the maximum value, excluding sampled values ​​lower than an arbitrary threshold value, or extracting sampled values ​​in a range where the magnitude of signal fluctuation is small.

[0032] When a device capable of setting the exposure time, such as a camera, is used as the photodetector 120, the output D r The detection intensity b r It can be said that:

[0033] Detection signal D r From detection intensity b r The conversion to may be performed by the processor 130 or may be performed outside the processor 130.

[0034] The reconstruction processing unit 134 of the arithmetic processing device 130 performs a process of generating a restored image by dividing M irradiations into a plurality of k (k≧2) units. Specifically, the reconstruction processing unit 134 performs a correlation calculation for each divided unit to generate an intermediate image M j (x, y) are generated. Then, k intermediate images M1 to M2 are generated for the k units. k (x, y) are synthesized to generate the final restored image G(x, y).

[0035] For simplicity, the number of irradiations n for each unit is assumed to be equal, M / k. In this case, the restored image G(x, y) is expressed by the following equation (2). r is the rth intensity distribution, and b r is the rth detection intensity value, r [j] > is the detection intensity b measured in the jth unit r is the average value.

number

[0036] The j-th term on the right-hand side of equation (2) is the intermediate image M j (x,y). Therefore, the restored image G(x,y) is a set of intermediate images M j The pixels corresponding to (x, y) are combined by simple addition. Note that the method of combination is not limited to simple addition, and weighted addition or other processing may be used.

[0037] The arithmetic processing device 130 can be implemented as a combination of a processor (hardware) such as a CPU (Central Processing Unit), MPU (Micro Processing Unit), or microcomputer, and a software program executed by the processor (hardware). The arithmetic processing device 130 may be a combination of multiple processors. Alternatively, the arithmetic processing device 130 may be configured only by hardware.

[0038] The above is the configuration of the imaging device 100. Next, the operation of the imaging device 100 will be described.

[0039] 3 is a time chart illustrating the operation of imaging apparatus 100. In FIG. 3, the total number of pixels of patterning device 114 is represented by p=4×4=16 pixels, and M random patterns I1 to I M is generated.

[0040] The reconstructed image is generated by dividing it into k units, each of which contains n exposures.

[0041] The first unit includes the first to nth irradiations, and the detection intensities b1 to b2 corresponding to the nth irradiation are n are generated and their average r [1] > is generated. Then, the detection strength b1~b n and their average r [1] > and intensity distribution I n+1 ~I 2n ​​Then, a correlation calculation is performed using the above to generate an intermediate image M1(x,y).

[0042] The second unit includes the n+1 to 2n irradiations, and the detection intensity b corresponding to the n irradiations n+1 ~b 2n are generated and their average r [2] > is generated. And the detection strength b n+1 ~b 2n and their average r [2] > and intensity distribution I n+1 ~I 2n Then, a correlation calculation is performed using the above to generate an intermediate image M2(x,y).

[0043] Similarly, the j-th unit includes the (j-1)n+1 to jn-th irradiations, and the detection intensity b (j-1)n+1 ~b jn are generated and their average r [j] > is generated. And the detection strength b (j-1)n+1 ~b jn and their average r [j] > and intensity distribution I (j-1)n+1 ~I jn Then, the correlation calculation is performed to obtain the intermediate image M j (x,y) is generated.

[0044] The last k-th unit includes the (k-1)n+1 to kn-th irradiations, and the detection intensity b (k-1)n+1 ~b kn are generated and their average r [k] > is generated. And the detection strength b (k-1)n+1 ~b kn and their average r [k] > and intensity distribution I (k-1)n+1 ~I kn Then, the correlation calculation is performed to obtain the intermediate image M k (x,y) is generated.

[0045] ​​​​​​Then, k intermediate images M1(x,y)~M k The final restored image G(x,y) is generated by combining (x,y).

[0046] The above is the operation of the imaging device 100. According to this imaging device 100, by dividing the correlation calculation into units, it is possible to reduce the amount of noise change per correlation calculation, thereby increasing the restoration accuracy and improving the image quality. In addition, since the correlation calculation can be started without waiting for the completion of M irradiations, it is possible to shorten the time required to generate a restored image.

[0047] The following describes the improvement of noise resistance by the imaging device 100. Hereinafter, imaging using correlation calculation for each unit based on equation (2) is referred to as divided restoration. Also, conventional imaging using correlation calculation based on equation (1) is referred to as collective restoration.

[0048] For example, consider noise that increases monotonically with time (called linear noise). Such linear noise can occur when the distance between a noise source and a photodetector decreases with time.

[0049] FIG. 4(a) is a diagram showing the effect of noise in collective restoration, and FIG. 4(b) is a diagram showing the effect of noise in divided restoration. The horizontal axis represents the number of the irradiation pattern, i.e., time. σ r represents the noise intensity when the rth pattern is irradiated, where σ r =α×r.

[0050] See Fig. 4(a). When performing collective restoration, the intensity of noise detected when each pattern is irradiated is b r and the mean value of the noise < σ r > difference Δσ r (=σ r -<σ r >) absolute value of |Δσ r The integrated value of | over the entire irradiation corresponds to the hatched area and is expressed by the following equation (3). Σ|Δσ r |=2×1 / 2×(kn / 2)×(knα / 2)=n 2 αk 2 / 4 …(3)

[0051] See Figure 4(b). Here, k = 5. When performing split restoration, the difference Δσ r (=σ r -<σ r [j] >) absolute value of |Δσ r |integrated value Σ|Δσ r | corresponds to the hatched area and is expressed by equation (4). Σ|Δσ r |=2×1 / 2×(n / 2)×(nα / 2)=n 2 α / 4 …(4) The integrated value over the entire irradiation can be calculated by multiplying the value of equation (4) by k, kn 2 The result is α / 4.

[0052] In this way, by performing division and restoration, the absolute value of the noise intensity difference |Δσ r The integrated value of | becomes smaller, thereby improving noise resistance.

[0053] Next, the results of simulations regarding batch restoration and divided restoration will be explained. Fig. 5(a) shows a target image, and Fig. 5(b) shows images obtained by collective restoration and divided restoration when there is no noise. The simulation was performed for M=1000, M=10000, and M=100000. The number of units k is set to 10.

[0054] In the case of batch restoration, M=100,000 is required to restore the original target image to a degree that makes it possible to recognize it, and the same is true for the case of partial restoration. At M=100,000, the accuracy of the finally obtained restored image is equivalent between batch restoration and partial restoration.

[0055] FIG. 6 shows a restored image G(x,y) obtained by collective restoration and divided restoration when linear noise is present. The total number of irradiations is M=10000, and the average value of the detection intensity is r >=1000. The noise coefficient α is calculated for four cases: 0, 0.01, 0.1, and 1. α=0 corresponds to the absence of noise. For the division and restoration, calculations are performed for k=10, 100, and 1000.

[0056] As can be seen from the simulation results in Fig. 6, the noise resistance increases as the division number k increases.

[0057] (Embodiment 2) In the first embodiment, as shown in Fig. 3, an independent process is performed for each frame, and a new restored image is generated for each frame period. In contrast, in the second embodiment, the restored image is updated every time a new intermediate image M is generated.

[0058] 7 is a diagram for explaining an imaging method according to the second embodiment. In the second embodiment, the reference light is continuously irradiated, and correlation calculation is performed every n irradiations. Then, the process of generating a restored image G(x, y) by synthesizing the most recent k intermediate images M(x, y) is repeated for each unit. Note that the imaging method according to the second embodiment can be realized in terms of hardware by the imaging device 100 in FIG. 2, and the process of the reconstruction processing unit 134 may be modified.

[0059] Other aspects are the same as those of embodiment 1. According to the imaging method of embodiment 2, it is possible to increase the speed at which a restored image is generated.

[0060] The present invention has been described above based on an embodiment. This embodiment is merely an example, and it will be understood by those skilled in the art that various modifications are possible in the combination of each component and each processing step, and that such modifications are also within the scope of the present invention. Such modifications will be described below.

[0061] ​(Variation 1) In the embodiment, the number of irradiations per unit is equal, but this is not limited thereto, and the number of irradiations per unit does not have to be equal.

[0062] (Variation 2) In the embodiment, the number of units k is constant, but the number of units k may be dynamically controlled. By selecting an optimal number of units k according to the noise fluctuation speed and noise waveform, the image quality can be further improved.

[0063] (Variation 3) In the embodiment, illumination device 110 is configured by a combination of light source 112 and patterning device 114, but this is not limited thereto. For example, illumination device 110 may be configured by an array of multiple semiconductor light sources (LEDs (light emitting diodes) and LDs (laser diodes)) arranged in a matrix, and each semiconductor light source may be controlled to be turned on and off (or its brightness).

[0064] (Application) Next, a description will be given of applications of the imaging device 100. Fig. 8 is a block diagram of an object identification system 10. This object identification system 10 is mounted on a vehicle such as an automobile or motorcycle, and determines the type (category) of an object OBJ that exists around the vehicle.

[0065] The object identification system 10 includes an imaging device 100 and a processor 40. As described above, the imaging device 100 generates a restored image G of the object OBJ by irradiating the object OBJ with a reference light S1 and measuring the reflected light S2.

[0066] The processor 40 processes the output image G of the imaging device 100 and determines the position and type (category) of the object OBJ.

[0067] The classifier 42 of the arithmetic processing device 40 receives the image G as an input and determines the position and type of the object OBJ contained therein. The classifier 42 is implemented based on a model generated by machine learning. The algorithm of the classifier 42 is not particularly limited, but may be YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), R-CNN (Region-based Convolutional Neural Network), SPPnet (Spatial Pyramid Pooling), Faster R-CNN, DSSD (Deconvolution-SSD), Mask R-CNN, or an algorithm to be developed in the future.

[0068] The above is the configuration of the object identification system 10. By using the imaging device 100 as a sensor of the object identification system 10, the following advantages can be obtained.

[0069] The use of the imaging device 100, i.e., the quantum radar camera, significantly improves noise resistance. For example, when driving in rain, snow, or fog, it is difficult to recognize the object OBJ with the naked eye, but by using the imaging device 100, it is possible to obtain a restored image G of the object OBJ without being affected by rain, snow, or fog.

[0070] In addition, by using the imaging device 100, it is possible to reduce calculation delays. This makes it possible to provide low-latency sensing. In particular, in vehicle-mounted applications, there are cases in which the object OBJ moves at high speed, and therefore the benefit of low-latency sensing is extremely large.

[0071] 9 is a block diagram of an automobile equipped with the object identification system 10. The automobile 300 is equipped with headlights 302L and 302R. The imaging device 100 is built into at least one of the headlights 302L and 302R. The headlight 302 is located at the very front of the vehicle body, and is the most advantageous location for installing the imaging device 100 in terms of detecting surrounding objects.

[0072] 10 is a block diagram showing a vehicle lamp 200 equipped with an object detection system 210. The vehicle lamp 200, together with a vehicle-side ECU 304, constitutes a lamp system 310. The vehicle lamp 200 includes a light source 202, a lighting circuit 204, and an optical system 206. The vehicle lamp 200 is further provided with an object detection system 210. The object detection system 210 corresponds to the above-mentioned object identification system 10, and includes an imaging device 100 and a processing device 40.

[0073] The information on the object OBJ detected by the arithmetic processing device 40 may be used for light distribution control of the vehicle lamp 200. Specifically, the lamp side ECU 208 generates an appropriate light distribution pattern based on the information on the type and position of the object OBJ generated by the arithmetic processing device 40. The lighting circuit 204 and the optical system 206 operate to obtain the light distribution pattern generated by the lamp side ECU 208.

[0074] Furthermore, information regarding the object OBJ detected by the arithmetic processing device 40 may be transmitted to the vehicle-side ECU 304. The vehicle-side ECU may perform automatic driving based on this information.

[0075] The present invention has been described using specific terms based on the embodiments, but the embodiments merely show one aspect of the principles and applications of the present invention, and many modifications and changes in arrangement are permitted to the embodiments without departing from the spirit of the present invention as defined in the claims. [Industrial Applicability]

[0076] The present invention relates to an imaging device that utilizes ghost imaging. [Explanation of symbols]

[0077] OBJ... object, 10... object identification system, 40... processing device, 42... classifier, 100... imaging device, 110... lighting device, 112... light source, 114... patterning device, 120... photodetector, 130... processing device, 132... pattern generator, 134... reconstruction processing unit, 200... vehicle lamp, 202... light source, 204... lighting circuit, 206... optical system, 300... automobile, 302... headlamp, 310... lamp system, 304... vehicle side ECU

Claims

1. an illumination device that sequentially switches and irradiates reference light having a spatially random intensity distribution; a photodetector for measuring reflected light from an object; a processor that performs correlation calculation between a detection intensity based on an output of the photodetector and an intensity distribution of the reference light, and reconstructs a restored image of the object; Equipped with The arithmetic processing device includes: M (M≧2) reference beams are divided into a plurality of k (k≧2) units; For the j-th unit, the number of the multiple reference beams included in the unit is n j (where n j ≧2), the intensity distribution of the r-th reference beam (1≦r≦n j ) is I r , and the detected intensity of the r-th reference beam is b r . Then, a correlation calculation is performed based on the formula (1) to generate an intermediate image M j ; [0010] The k intermediate images M obtained for the k units １ ~M ｋ to generate the restored image; M is the number of images G calculated based on formula (2) that can restore the original image when the irradiation is not divided and the image is restored in a single operation under noise-free conditions, [0025] The imaging device is characterized in that the number n 1 to n k of the reference beams included in one unit is smaller than the number of intermediate images M 1 to M k that can restore the original image under noise-free conditions.

2. 2. The imaging apparatus according to claim 1, wherein the numbers of reference beams n 1 to n k included in each unit are equal to M / k.

3. 3. The imaging device according to claim 1, wherein the number k of units is dynamically controlled.

4. an illumination device that sequentially switches between and irradiates M reference beams having a spatially random intensity distribution; a photodetector for measuring reflected light from an object; a processor that performs correlation calculation between a detection intensity based on an output of the photodetector and an intensity distribution of the reference light, and reconstructs a restored image of the object; Equipped with The arithmetic processing device regards n (n≧2) reference beams as one unit, and each time one unit is irradiated, performs correlation calculation based on Equation (3) to generate an intermediate image Mj, where Ir is the intensity distribution of the r-th (1≦r≦n) reference beam and b r is the detected intensity of the r-th reference beam; [0030] synthesizing the latest k (k≧2) intermediate images to generate the restored image; The number n of reference beams included in one unit is smaller than the number of intermediate images M j that can restore the original image under noise-free conditions, An imaging device characterized in that k×n=M is ​​a number of images G calculated based on equation (4) that can restore the original image when the irradiation is not divided and the image is restored all at once under noise-free conditions. [0045]

5. 5. The imaging apparatus according to claim 1, wherein the synthesis of the intermediate images includes a simple addition process of corresponding pixel values.

6. A vehicle lamp comprising the imaging device according to claim 1 .

7. A vehicle comprising an imaging device according to any one of claims 1 to 6.

8. A step of sequentially switching reference light having a random intensity distribution and irradiating the object; measuring light reflected from the object with a light detector; Dividing M (M≧2) reference beams into a plurality of k (k≧2) units; a step of generating an intermediate image Mj by performing correlation calculation based on formula (1) for a j-th unit, where nj (nj≧2) is the number of reference beams included in the unit, Ir is the intensity distribution of the r-th reference beam (1≦r≦nj), and br is the detection intensity based on the output of the photodetector obtained as a result of the r-th irradiation; [0050] A step of synthesizing the k intermediate images M 1 to M k obtained for the k units to generate a restored image; Equipped with M is the number of images G calculated based on formula (2) that can restore the original image when the irradiation is not divided and restored collectively under noise-free conditions, [006] The imaging method is characterized in that the number n 1 to nk of the reference beams included in one unit is smaller than the number of intermediate images M 1 to M k that can restore the original image under noise-free conditions.

9. A step of sequentially switching reference light having a spatially random intensity distribution and irradiating the object; measuring light reflected from the object with a light detector; a step of performing a correlation calculation based on equation (3) to generate an intermediate image Mj each time one unit is irradiated, where Ir is the intensity distribution of the rth (1≦r≦n) reference light and b r is the detection intensity based on the output of the photodetector obtained as a result of the rth irradiation, where n (n≧2) reference light beams are one unit; [0070] A step of synthesizing the latest k (k≧2) intermediate images to generate a restored image; Equipped with The number n of reference beams included in one unit is smaller than the number of intermediate images M j that can restore the original image under noise-free conditions, An imaging method characterized in that k×n=M is ​​a number of images G calculated based on equation (4) that can restore the original image when the irradiation is not divided and the image is restored all at once under noise-free conditions. [0080]

Images