Imaging capability evaluation method, device and equipment and computer readable storage medium

Abstract

The application discloses an imaging capability evaluation method, device and equipment and a computer readable storage medium. The method comprises the following steps: obtaining a square test image by shooting a square chart by using an imaging device, wherein the square test image is distributed according to a field of view; extracting a region of interest corresponding to each field of view in the square test image, and processing the direction of the region of interest to obtain a processed region, wherein the light-dark boundary of the processed region is vertical, and the left side of the light-dark boundary is a dark area; calculating a spatial frequency response curve according to the light-dark boundary, and evaluating the imaging capability of the imaging device in each field of view based on the spatial frequency response curve. The application realizes the improvement of the accuracy of the imaging capability of a test imaging device.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and in particular to an imaging capability evaluation method, apparatus, device, and computer-readable storage medium. Background Technology

[0002] As consumers pursue a higher quality of life, lower image quality no longer meets their aesthetic standards, making image quality an urgent issue for the camera industry to optimize. The imaging device market is highly competitive, with major suppliers shifting their focus from early hardware stacking to the algorithm field, increasingly emphasizing maximizing the performance of the hardware itself. Therefore, testing the imaging capabilities of imaging devices has gradually become an indispensable part of the manufacturing process.

[0003] Currently, the main method is to visually assess the image quality using test cards. This method relies on the tester's subjective perception and has certain limitations. Alternatively, the image quality can be indirectly judged using comprehensive imaging quality evaluation methods such as RMS (Root Mean Square). However, this testing method cannot directly reflect the imaging capabilities of the imaging device. Summary of the Invention

[0004] The main objective of this invention is to provide an imaging capability evaluation method, apparatus, device, and computer-readable storage medium, with the aim of providing an imaging capability detection method for imaging devices to improve the accuracy of testing the imaging capability of imaging devices.

[0005] To achieve the above objectives, the present invention provides an imaging capability evaluation method, which includes the following steps:

[0006] A grid test image is obtained by capturing a grid chart using an imaging device, wherein the grid test image is distributed according to the field of view;

[0007] Extract the region of interest corresponding to each field of view in the grid test image, and process the direction of the region of interest to obtain the processed region, wherein the light and dark boundaries of the processed region are vertical and the left side of the light and dark boundary is the dark area.

[0008] The spatial frequency response curve is calculated based on the light and dark boundaries, and the imaging capability of the imaging device in each field of view is evaluated based on the spatial frequency response curve.

[0009] To achieve the above objectives, the present invention also provides an imaging capability evaluation device, the imaging capability evaluation device comprising:

[0010] The imaging module is used to capture a grid test image of a grid card using an imaging device, wherein the grid test image is distributed according to the field of view.

[0011] The processing module is used to extract the region of interest corresponding to each field of view in the grid test image, and process the direction of the region of interest to obtain the processed region, wherein the light and dark boundary of the processed region is vertical and the left side of the light and dark boundary is a dark area.

[0012] The evaluation module is used to calculate the spatial frequency response curve based on the light and dark boundaries, and to evaluate the imaging capability of the imaging device in each field of view based on the spatial frequency response curve.

[0013] To achieve the above objectives, the present invention also provides an imaging capability evaluation device, the imaging capability evaluation device comprising: a memory, a processor, and an imaging capability evaluation program stored in the memory and executable on the processor, wherein the imaging capability evaluation program, when executed by the processor, implements the steps of the imaging capability evaluation method as described above.

[0014] Furthermore, to achieve the above objectives, the present invention also proposes a computer-readable storage medium storing an imaging capability evaluation program, which, when executed by a processor, implements the steps of the imaging capability evaluation method as described above.

[0015] In this invention, a grid test image is obtained by capturing a grid chart using an imaging device, wherein the grid test image is distributed according to the field of view; a region of interest corresponding to each field of view is extracted from the grid test image, and the direction of the region of interest is processed to obtain a processed region, wherein the light and dark boundaries of the processed region are vertical and the left side of the light and dark boundary is a dark area; a spatial frequency response curve is calculated based on the light and dark boundaries, and the imaging capability of the imaging device in each field of view is evaluated based on the spatial frequency response curve.

[0016] This invention enables the imaging capability of an imaging device across various fields of view to be reflected through spatial frequency response curves. Compared to objective judgment through visual observation or RMS, this invention can intuitively demonstrate the imaging capability of the device across different fields of view using modulation transfer function values, thus improving the accuracy of testing the imaging capability of the device. Furthermore, this invention distinguishes between spatial frequency response curves under different fields of view. Compared to spatial frequency response curves without field-of-view differentiation, this invention achieves a multi-faceted evaluation of the imaging capability of the device, making the evaluation more detailed and comprehensive, thereby improving the accuracy of imaging capability assessment. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the hardware operating environment involved in the embodiments of the present invention;

[0018] Figure 2 This is a flowchart illustrating the first embodiment of the imaging capability evaluation method of the present invention;

[0019] Figure 3 This is a schematic diagram of the field of view distribution of a grid test image involved in an embodiment of the imaging capability evaluation method of the present invention;

[0020] Figure 4 This is a schematic diagram of image preprocessing involved in an embodiment of the imaging capability evaluation method of the present invention;

[0021] Figure 5 This is a schematic diagram of the oversampling process involved in an embodiment of the imaging capability evaluation method of the present invention;

[0022] Figure 6 This is a spatial frequency response curve related to an embodiment of the imaging capability evaluation method of the present invention;

[0023] Figure 7 This is a schematic diagram of the differential curve adjustment involved in an embodiment of the imaging capability evaluation method of the present invention;

[0024] Figure 8 This is a schematic diagram of the differential curve adjustment involved in an embodiment of the imaging capability evaluation method of the present invention;

[0025] Figure 9 This is a schematic diagram of the normalization process involved in an embodiment of the imaging capability evaluation method of the present invention;

[0026] Figure 10 This is a schematic diagram of the functional modules of a preferred embodiment of the imaging capability evaluation device of the present invention.

[0027] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0028] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0030] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0031] In this invention, unless otherwise explicitly specified and limited, the terms "connection," "fixed," etc., should be interpreted broadly. For example, "fixed" can mean a fixed connection, a detachable connection, or an integral part; it can mean a mechanical connection or an electrical connection; it can mean a direct connection or an indirect connection through an intermediate medium; it can mean the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0032] Furthermore, in this invention, descriptions involving "first," "second," etc., are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed by this invention.

[0033] like Figure 1 As shown, Figure 1 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of the present invention.

[0034] like Figure 1 As shown, in the hardware operating environment of the imaging capability evaluation device, the device may include: a processor 1001, such as a CPU; a network interface 1004; a user interface 1003; a memory 1005; and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen and an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or stable non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0035] Those skilled in the art will understand that Figure 1 The device structure shown does not constitute a limitation on the imaging capability evaluation device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0036] like Figure 1 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an imaging capability evaluation program.

[0037] exist Figure 1 In the device shown, network interface 1004 is mainly used to connect to the backend server and communicate data with it; user interface 1003 is mainly used to connect to the client (user terminal) and communicate data with it; while processor 1001 can be used to call the optomechanical imaging quality analysis program stored in memory 1005 and perform the following operations:

[0038] A grid test image is obtained by capturing a grid chart using an imaging device, wherein the grid test image is distributed according to the field of view;

[0039] Extract the region of interest corresponding to each field of view in the grid test image, and process the direction of the region of interest to obtain the processed region, wherein the light and dark boundaries of the processed region are vertical and the left side of the light and dark boundary is the dark area.

[0040] The spatial frequency response curve is calculated based on the light and dark boundaries, and the imaging capability of the imaging device in each field of view is evaluated based on the spatial frequency response curve.

[0041] Optionally, the processor 1001 can be used to call the optomechanical imaging quality analysis program stored in the memory 1005 and perform the following operations:

[0042] Calculate the linear regression line of the light and dark boundary;

[0043] The sampling range for oversampling the processed region is determined based on the boundary slope of the linear regression line, and the processed region is oversampled according to the sampling range to obtain oversampled data;

[0044] The oversampled data is differentially calculated to obtain a first difference curve, and the first difference curve is processed to obtain a target curve;

[0045] The spatial frequency response curve is obtained by adjusting the horizontal axis coordinate of the target curve.

[0046] Optionally, the processor 1001 can be used to call the optomechanical imaging quality analysis program stored in the memory 1005 and perform the following operations:

[0047] Determine the grayscale value of each pixel in the processed region, and calculate the grayscale difference between two adjacent pixels in each row;

[0048] The second difference curve for each row of pixels is obtained based on the grayscale difference value corresponding to each row of pixels;

[0049] Calculate the first centroid of the second difference curve, and obtain the linear regression line of the light and dark boundary by linear fitting through each of the first centroids.

[0050] Optionally, the processor 1001 can be used to call the optomechanical imaging quality analysis program stored in the memory 1005 and perform the following operations:

[0051] The second difference curve is subjected to Hamming window filtering to obtain the third difference curve, and the first centroid of the third difference curve is calculated.

[0052] Optionally, the processor 1001 can be used to call the optomechanical imaging quality analysis program stored in the memory 1005 and perform the following operations:

[0053] Determine the regression position of each of the first centroids mapped onto the linear regression line, and perform Hamming window filtering on each of the second difference curves with each of the regression positions as the center to obtain the fourth difference curve;

[0054] Determine the second centroid of the fourth difference curve, and perform a quadratic fitting based on each of the second centroids to obtain a quadratic fitted line;

[0055] The quadratic fitted line is used as the linear regression line, and the step of determining the sampling range for oversampling the processed region based on the boundary slope of the linear regression line is performed.

[0056] Optionally, the processor 1001 can be used to call the optomechanical imaging quality analysis program stored in the memory 1005 and perform the following operations:

[0057] Detect whether imaging aberrations occur in the grid test image;

[0058] After the step of obtaining the linear regression line of the light and dark boundaries by linear fitting through each of the first centroids, the method further includes:

[0059] If the grid test image has imaging aberrations, then the step of determining the regression position of each of the first centroids mapped onto the linear regression line is performed;

[0060] If no imaging aberration occurs in the grid test image, then the step of determining the sampling range for oversampling the processed region based on the boundary slope of the linear regression line is performed.

[0061] Optionally, the processor 1001 can be used to call the optomechanical imaging quality analysis program stored in the memory 1005 and perform the following operations: perform Hamming window filtering on the first difference curve with the third centroid of the first difference curve as the center to obtain the filtered curve;

[0062] Performing a Fourier transform on the filtered curve yields an axisymmetric transformed curve.

[0063] The normalized curve is obtained by normalizing half of the data from the transformed curve.

[0064] The target curve is obtained by amplifying the high-frequency portion of the normalized curve.

[0065] Based on the above hardware structure, the overall concept of various embodiments of the optomechanical imaging quality analysis method of the present invention is proposed.

[0066] In this embodiment of the invention, as consumers pursue a higher quality of life, lower image quality no longer meets their aesthetic standards. Improving imaging quality is a pressing issue that the imaging device industry needs to address. The imaging device market is highly competitive, and the focus of competition among major suppliers has gradually shifted from early hardware stacking to the field of algorithms, with increasing emphasis on maximizing the performance of the hardware itself. Therefore, testing the imaging capabilities of imaging devices has gradually become an indispensable step in the production process.

[0067] Currently, the main method is to visually assess the image quality using test cards. This method relies on the tester's subjective perception and has certain limitations. Alternatively, the image quality can be indirectly judged using comprehensive imaging quality evaluation methods such as RMS (Root Mean Square). However, this testing method cannot directly reflect the imaging capabilities of the imaging device.

[0068] To address the aforementioned problems, embodiments of the present invention provide an imaging capability evaluation method, which includes: capturing a grid test image using an imaging device, wherein the grid test image is distributed according to the field of view; extracting the region of interest corresponding to each field of view from the grid test image, and processing the direction of the region of interest to obtain a processed region, wherein the light and dark boundaries of the processed region are vertical and the left side of the light and dark boundaries is a dark area; calculating a spatial frequency response curve based on the light and dark boundaries, and evaluating the imaging capability of the imaging device in each field of view based on the spatial frequency response curve.

[0069] This invention enables the imaging capability of an imaging device across various fields of view to be reflected through spatial frequency response curves. Compared to objective judgment through visual observation or RMS, this invention can intuitively demonstrate the imaging capability of the device across different fields of view using modulation transfer function values, thus improving the accuracy of testing the imaging capability of the device. Furthermore, this invention distinguishes between spatial frequency response curves under different fields of view. Compared to spatial frequency response curves without field-of-view differentiation, this invention achieves a multi-faceted evaluation of the imaging capability of the device, making the evaluation more detailed and comprehensive, thereby improving the accuracy of imaging capability assessment.

[0070] Based on the overall concept of the optomechanical imaging quality analysis method of the present invention described above, various embodiments of the optomechanical imaging quality analysis method of the present invention are proposed.

[0071] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the imaging capability evaluation method of the present invention. It should be noted that although the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0072] In this embodiment, the executing entity of the imaging capability evaluation method can be a personal computer, smartphone, or other device; no limitation is imposed in this embodiment. For ease of description, the executing entity is omitted from the description of each embodiment below. In this embodiment, the imaging capability evaluation method includes:

[0073] Step S10: Obtain a grid test image by photographing a grid card using an imaging device, wherein the grid test image is distributed according to the field of view;

[0074] In this embodiment, a grid chart (hereinafter referred to as the grid chart for convenience) is used as a test chart for imaging capability detection. During the test, an imaging device captures an image of the test chart to obtain an image for imaging capability detection (hereinafter referred to as the grid test image for distinction). Specifically, in this embodiment, the grid chart is distributed according to the field of view, with each square in the same field of view arranged in a circle. After obtaining the grid test image, the region of interest (ROI) corresponding to each field of view is extracted from the grid test image captured by the imaging device. When extracting ROI, there are no fewer than two ROIs for each field of view. Specifically, the specific method for extracting ROIs is not limited here. It can be done by using data structures in OpenCV (a cross-platform computer vision library) or by extracting ROIs through machine learning, which will not be elaborated here.

[0075] For example, please refer to Figure 3 , Figure 3This is a schematic diagram of the field of view distribution of a grid test image involved in an embodiment of the imaging capability evaluation method of the present invention, as shown below. Figure 3 As shown, in this embodiment, the grid chart is arranged according to the central field of view (i.e., Figure 3 The diagram shows the distribution of CenF, 0.5 field of view (0.5F), and 0.8 field of view (0.8F), with each square in the same field of view arranged in a circle.

[0076] Step S20: Extract the region of interest corresponding to each field of view in the grid test image, and process the direction of the region of interest to obtain the processed region, wherein the light and dark boundary of the processed region is vertical and the left side of the light and dark boundary is the dark area;

[0077] In this embodiment, the direction of the region of interest (ROI) is processed to obtain the processed region. Each ROI contains a sloping edge that marks the boundary between light and dark areas; this sloping edge in the processed region will be referred to as the light-dark boundary for distinction. In this embodiment, the direction of the ROI is processed as follows: the light-dark boundary of the ROI is adjusted to be vertical, and the left side of the light-dark boundary is a dark area, while the right side is a bright area. Therefore, the light-dark boundary of the processed region is vertical, and the left side of the light-dark boundary in the processed region is a dark area.

[0078] For example, please refer to Figure 4 , Figure 4 This is a schematic diagram of image preprocessing involved in an embodiment of the imaging capability evaluation method of the present invention, as shown below. Figure 4 As shown, in this embodiment, the light and dark boundary of the region of interest is adjusted to be vertical, and the left side of the light and dark boundary is a dark area and the right side is a bright area.

[0079] Step S30: Calculate the spatial frequency response curve based on the light and dark boundaries, and evaluate the imaging capability of the imaging device in each field of view based on the spatial frequency response curve.

[0080] In this embodiment, after determining the region of interest, the spatial frequency response curve is calculated based on the light and dark boundaries.

[0081] In this embodiment, after determining the spatial frequency response curve, the imaging capability of the imaging device in the target field of view is evaluated based on the spatial frequency response curve.

[0082] In specific implementations, the imaging capability of the imaging device can be determined based on the magnitude of the modulation transfer function value at the corresponding spatial frequency. For example, when the modulation transfer function value is greater than a preset threshold, the imaging capability of the imaging device in the target field of view is determined to be qualified. Alternatively, the imaging capability of the imaging device can be determined by comparing the difference between the spatial frequency response curve of the imaging device in the target field of view and the standard spatial frequency response curve. It is understood that the higher the spatial frequency response value corresponding to the same spatial frequency, the clearer the image, and the better the imaging capability of the imaging device.

[0083] Optionally, in a feasible embodiment, step S30 above: calculating the spatial frequency response curve based on the light and dark boundary, includes:

[0084] Step S301: Calculate the linear regression line of the light and dark boundary;

[0085] For image data, each row at the boundary between light and dark areas can be considered as an estimate of the edge spread function. These estimates of the edge spread function can be used to obtain the corresponding line spread function through difference calculation. The spatial frequency response curve can be obtained by performing a Fourier transform on the line spread function.

[0086] Therefore, in this embodiment, the linear regression line of the light and dark boundary in the processed area is calculated. In a specific implementation, the centroid of each row of pixels can be calculated first, and the linear regression line of the light and dark boundary in the processed area can be obtained by fitting the centroid.

[0087] Step S302: Determine the sampling range for oversampling the processed region based on the boundary slope of the linear regression line, and oversample the processed region according to the sampling range to obtain oversampled data;

[0088] In this embodiment, after obtaining the linear regression line, oversampling is performed based on the linear regression line to obtain a more refined light-dark boundary line for black-and-white transformation. Specifically, in this embodiment, after determining the linear regression line, the slope of the light-dark boundary (hereinafter referred to as the boundary slope for distinction) can be determined based on the linear regression line. The sampling range for oversampling the processed region is determined based on the boundary slope of the linear regression line, and the processed region is oversampled according to the sampling range to obtain oversampled data. Specifically, the oversampled data is the edge diffusion sequence. In a specific implementation, the oversampling factor can be set according to actual needs; for example, in one implementation, it can be 4x sampling processing.

[0089] Step S303: Perform differential calculation on the oversampled data to obtain a first differential curve, and process the first differential curve to obtain a target curve;

[0090] In this embodiment, after obtaining the oversampled data, a difference curve is obtained by performing a difference calculation on the oversampled data. This difference curve is referred to below as the first difference curve for distinction, and it is the line diffusion sequence. Specifically, the oversampling process is as follows: the integer multiple of the reciprocal of the boundary slope is determined as the actual number of sampling rows for oversampling the processed region; oversampled data is obtained by sampling the region to be sampled corresponding to the actual number of sampling rows using pixels of a preset number of rows as a group of pixels. The preset number of rows can be set according to actual needs.

[0091] For example, when performing 4x sampling, the oversampled data can be obtained by sampling the area to be sampled in the processed region corresponding to the actual number of sampling rows, with 4 rows of pixels as a group of pixels. Specifically, please refer to... Figure 5 , Figure 5 This is a schematic diagram of the oversampling process involved in an embodiment of the imaging capability evaluation method of the present invention. Taking a set of pixels as an example, the process of performing 4x sampling can be as follows: read the gray values ​​of the first row of pixels, the second row of pixels, the third row of pixels, and the fourth row of pixels in sequence, and save them in order from left to right. Finally, rearrange the read data according to the rule of the first pixel in the first row, the first pixel in the second row, the first pixel in the third row, the first pixel in the fourth row, the second pixel in the first row, and the second pixel in the second row. The new data combination obtained is the oversampling data after 4x sampling.

[0092] By oversampling the processed area, compared with the regression line obtained by fitting a single row of pixels, this embodiment is less affected by noise and can obtain a more delicate boundary between light and dark areas, making the calculated modulation transfer function value more accurate, thereby making the evaluation of imaging capability more accurate.

[0093] In this embodiment, after obtaining the first difference curve, the first difference curve is processed to obtain a curve with modulation transfer function value, which is referred to as the target curve for distinction.

[0094] Step S304: Adjust the horizontal axis coordinate of the target curve to obtain the spatial frequency response curve.

[0095] The horizontal axis of the first difference curve can be represented by the number of actual pixels that it spans in the horizontal direction. The horizontal axis of the target curve is also represented by the number of actual pixels that it spans in the horizontal direction. The spatial frequency response curve is the correlation curve between frequency and modulation transfer function value.

[0096] In this embodiment, after obtaining the target curve, the horizontal axis coordinate of the target curve is adjusted to obtain the space frequency response curve that reflects the modulation transfer function values ​​at all spatial frequencies. For example, please refer to... Figure 6 , Figure 6This is a spatial frequency response curve related to an embodiment of the imaging capability evaluation method of the present invention. The horizontal axis of the frequency response curve represents frequency (i.e., Figure 6 The frequency shown is represented by the frequency on the vertical axis, and the MTF (Modulation Transfer Function) value is represented by the vertical axis. Figure 6 The SFR (Spatial Frequency Response) shown is illustrated in the figure.

[0097] In this embodiment, when the spatial frequency unit is line pairs per millimeter, the specific process of adjusting the horizontal axis coordinate can be as follows: determine the spatial frequency unit of the horizontal axis of the spatial frequency response curve; divide the full frequency by the pixel size of the imaging device and calculate the sampling frequency of the grid test image sampled by the imaging device; divide the sampling frequency by the horizontal axis width of the target curve to obtain the horizontal axis step size; multiply the horizontal axis width by the horizontal axis step size to calculate the maximum horizontal coordinate; adjust the horizontal axis coordinate of the target curve based on the horizontal axis step size and the maximum horizontal coordinate to obtain the spatial frequency response curve.

[0098] For example, assuming the pixel size of the imaging device (sensor) used is 1.5µm, the horizontal step size of the spatial frequency response curve is:

[0099] Step size = Sampling frequency / 1000 = 1000 / 1.5 / 1000 (LP / mm) = 0.667 (LP / mm)

[0100] In this embodiment, the frequency of the rightmost sampling point of the spatial frequency response curve is:

[0101] 1000*0.667(LP / mm)=667(LP / mm).

[0102] It should be noted that the horizontal axis coordinates in other spatial frequency units can be converted from the horizontal axis coordinates when the spatial frequency unit is per millimeter line pair.

[0103] Optionally, in a feasible embodiment, step S303 above: processing the first difference curve to obtain the target curve includes:

[0104] Step S3031: Apply Hamming window filtering to the first difference curve with the third centroid of the first difference curve as the center to obtain the filtered curve;

[0105] In this embodiment, the first difference curve is filtered using a Hamming window with the centroid of the first difference curve (hereinafter referred to as the third centroid) as the center to obtain the filtered curve. Specifically, when the third centroid is not at the center of the Hamming window, the first difference curve can be translated by adding a straight line with a slope of 1 so that the third centroid is located at the center of the Hamming window.

[0106] For example, please refer to Figure 7 , Figure 7 This is a schematic diagram of the differential curve adjustment involved in an embodiment of the imaging capability evaluation method of the present invention. When the third centroid is located to the left of the Hamming window, a straight line with a slope of 1 is added to the left of the first differential curve, and the third centroid is shifted to the center of the Hamming window; please refer to... Figure 8 , Figure 8 This is a schematic diagram of the differential curve adjustment involved in an embodiment of the imaging capability evaluation method of the present invention. When the third centroid is located to the right of the Hamming window, a straight line with a slope of 1 is added to the right of the first differential curve, and the third centroid is shifted to the center of the Hamming window.

[0107] Step S3032: Perform a Fourier transform on the filtered curve to obtain an axisymmetric transformed curve;

[0108] The filtered curve is subjected to a Fourier transform to obtain an axisymmetric curve (hereinafter referred to as the transformed curve for distinction).

[0109] Step S3033: Take half of the data of the transformed curve and normalize it to obtain the normalized curve;

[0110] The normalized curve is obtained by normalizing half of the data from the transformed curve. In this embodiment, since the transformed curve obtained by the Fourier transform is axisymmetric, only half of the data from the transformed curve needs to be processed. Specifically, as shown... Figure 9 As shown, Figure 9 This is a schematic diagram of the normalization process involved in an embodiment of the imaging capability evaluation method of the present invention, for the first difference curve (i.e. Figure 9 The difference curve shown in the figure is subjected to Fourier transform to obtain the transformed curve, and half of the data of the transformed curve is normalized.

[0111] Step S3034: Magnify the high-frequency part of the normalized curve to obtain the target curve.

[0112] The high-frequency portion of the normalized curve is amplified, and the resulting curve is called the target curve. Specifically, in this embodiment, the normalized curve is multiplied by the `correct` function to amplify the high-frequency portion of the normalized curve. In this specific implementation, the maximum value of the `correct` function is limited to 10.

[0113] In this embodiment, a grid test image is obtained by capturing a grid chart using an imaging device. The grid test image is distributed according to the field of view. The region of interest corresponding to each field of view is extracted from the grid test image, and the direction of the region of interest is processed to obtain a processed region. The light and dark boundaries of the processed region are vertical, and the left side of the light and dark boundary is a dark area. The spatial frequency response curve is calculated based on the light and dark boundaries, and the imaging capability of the imaging device in each field of view is evaluated based on the spatial frequency response curve.

[0114] This embodiment demonstrates the imaging capability of an imaging device across various fields of view using spatial frequency response curves. Compared to visual observation or objective judgment via RMS, this embodiment directly reflects the imaging capability of the device across different fields of view through modulation transfer function values, improving the accuracy of testing the imaging capability of the device. Furthermore, this embodiment distinguishes between spatial frequency response curves under different fields of view. Compared to spatial frequency response curves without field-of-view differentiation, this embodiment achieves a multi-faceted evaluation of the imaging capability of the device, resulting in a more detailed and comprehensive assessment.

[0115] Furthermore, based on the first embodiment of the imaging capability evaluation method of the present invention described above, a second embodiment of the imaging capability evaluation method of the present invention is proposed.

[0116] In this embodiment, step S301 above: calculating the linear regression line of the light and dark boundary includes:

[0117] Step S3011: Determine the gray value of each pixel in the processed region, and calculate the gray value difference between two adjacent pixels in each row;

[0118] In this embodiment, the linear regression line of the light and dark boundary is calculated using the finite difference method. Specifically, the gray value of each pixel in the processed region is determined, and the gray value difference between two adjacent pixels in each row is calculated.

[0119] Step S3012: Obtain the second difference curve corresponding to each row of pixels based on the grayscale difference value corresponding to each row of pixels;

[0120] Based on the grayscale difference value corresponding to each row of pixels, the difference curve corresponding to each row of pixels is obtained. It is referred to as the second difference curve for distinction.

[0121] Specifically, in this embodiment, the formula for calculating the grayscale difference is: dri[i]=(data[i+1]–data[i]) / 2, where i is the selected pixel and i+1 is the next pixel.

[0122] Step S3013: Calculate the first centroid of the second difference curve, and obtain the linear regression line of the light and dark boundary by linear fitting through each of the first centroids.

[0123] In this embodiment, after obtaining the second difference curve, the centroid of each second difference curve (hereinafter referred to as the first centroid) is calculated, and the linear regression line of the light and dark boundary is obtained by linear fitting through each first centroid.

[0124] In a specific implementation, the first centroid can be calculated using the following formula, specifically:

[0125]

[0126] Where n is the number of sample points, and x is the x-coordinate of the sample point in the second difference curve. It can be understood that the y-coordinate of the first centroid can be calculated in the same way.

[0127] Optionally, in a feasible embodiment, the second difference curve can be filtered and denoised before calculating the first centroid to reduce interference signals, so that the difference curve obtained after filtering is smoother.

[0128] Optionally, in a feasible embodiment, step S3013 above: calculating the first centroid of the second difference curve includes:

[0129] Step S30131: Perform Hamming window filtering on the second difference curve to obtain the third difference curve, and calculate the first centroid of the third difference curve.

[0130] In this embodiment, the second difference curve is filtered using a Hamming window to obtain the third difference curve, and the first centroid of each third difference curve is calculated. It should be noted that in this embodiment, the x-coordinate of the center of the Hamming window is the same as the median x-coordinate of the first difference curve during filtering.

[0131] It should be noted that in this embodiment, Hamming window filtering can reduce interference signals, making the third difference curve obtained after filtering smoother, making the coordinates of the first centroid more accurate, making the subsequent fitted regression line more accurate, thereby making the obtained spatial frequency response curve more accurate and improving the accuracy of imaging capability evaluation.

[0132] Optionally, in a feasible embodiment, after step S3013 above: obtaining the linear regression line of the light and dark boundaries by linear fitting through each of the first centroids, the method further includes:

[0133] Step S3014: Determine the regression position of each of the first centroids mapped onto the linear regression line, and perform Hamming window filtering on each of the second difference curves with each regression position as the center to obtain the fourth difference curve;

[0134] In this embodiment, a second fitting is performed on the light and dark boundaries in the processed area to make the regression line of the obtained light and dark boundaries more accurate.

[0135] Specifically, in this embodiment, the position of each first centroid mapped onto the linear regression line is determined, hereinafter referred to as the regression position for distinction. A Hamming window filter is applied to each second difference curve centered on each regression position to obtain a fourth difference curve. It should be noted that applying the Hamming window filter centered on the regression position makes the obtained fourth difference curve more accurate, resulting in a more accurate regression line for subsequent fitting, and thus a more accurate spatial frequency response curve, thereby improving the accuracy of imaging capability evaluation.

[0136] Step S3015: Determine the second centroid of the fourth difference curve, and perform a second fitting based on each of the second centroids to obtain a second fitting line;

[0137] The centroids of each fourth difference curve are determined (hereinafter referred to as the second centroids for distinction), and a quadratic fitting is performed based on each second centroid to obtain the quadratic fitted line.

[0138] Step S3016: Use the quadratic fitted line as the linear regression line, and perform the step of determining the sampling range for oversampling the processed region based on the boundary slope of the linear regression line.

[0139] The quadratic fitted line is used as the linear regression line, and the step of determining the sampling range for oversampling the processed region based on the boundary slope of the linear regression line is performed.

[0140] It should be noted that in this embodiment, by performing a second fitting on the light and dark boundaries in the processed area, compared to performing only a single fitting, this embodiment can make the regression line of the obtained light and dark boundaries more accurate, thereby making the boundary slope more accurate, and thus making the obtained spatial frequency response curve more accurate, which can improve the accuracy of imaging capability evaluation.

[0141] Optionally, in a feasible embodiment, the imaging capability evaluation method further includes:

[0142] Step S40: Detect whether imaging aberrations occur in the grid test image;

[0143] In this embodiment, imaging aberrations are detected in the grid test image to determine whether the light and dark boundaries need to be fitted twice. The specific method for detecting imaging aberrations will not be described in detail here.

[0144] It is understood that, in this embodiment, imaging aberrations include spherical aberration, coma, field curvature, astigmatism, distortion, chromatic aberration, and wave aberration.

[0145] In this embodiment, after step S3013 above: obtaining the linear regression line of the light and dark boundaries by linear fitting through each of the first centroids, the method further includes:

[0146] Step S3017: If the grid test image has imaging aberrations, then perform the step of determining the regression position of each of the first centroids mapped onto the linear regression line;

[0147] In this embodiment, if imaging aberration occurs in the grid test image, a second fitting is performed on the light and dark boundaries to improve the accuracy of the slope of the obtained light and dark boundaries. That is, after obtaining the linear regression line of the light and dark boundaries by linear fitting through each first centroid, the step of determining the regression position of each first centroid on the linear regression line is executed.

[0148] Step S3018: If no imaging aberration occurs in the grid test image, then perform the step of determining the sampling range for oversampling the processed region based on the boundary slope of the linear regression line.

[0149] If no imaging aberration occurs in the grid test image, only one fitting is needed for the light and dark boundaries. That is, after obtaining the linear regression line of the light and dark boundaries through linear fitting of each first centroid, the step of determining the sampling range for oversampling the processed region based on the boundary slope of the linear regression line is executed.

[0150] It should be noted that, compared to directly performing secondary fitting on the light and dark boundaries, this embodiment performs secondary fitting when imaging aberrations are determined to occur in the grid test image, which can reduce the workload in the imaging capability evaluation process and improve work efficiency.

[0151] In this embodiment, by determining the gray value of each pixel in the processed region and calculating the gray value difference between two adjacent pixels in each row, the second difference curve corresponding to each row of pixels is obtained based on the gray value difference corresponding to each row of pixels. The first centroid of the second difference curve is calculated, and the linear regression line of the light and dark boundary is obtained by linear fitting through each first centroid.

[0152] This embodiment determines the boundary slope of the light and dark boundary, and thus the spatial frequency response can be calculated based on the boundary slope to obtain the spatial frequency response curve. This allows the imaging capability of the imaging device in each field of view to be reflected through the spatial frequency response curve. Compared with visual observation or objective judgment through RMS, this embodiment can intuitively reflect the imaging capability of the imaging device in each field of view through the modulation transfer function value, thereby improving the accuracy of testing the imaging capability of the imaging device.

[0153] For example, the entire process of determining the spatial frequency response curve can be as follows:

[0154] The image is preprocessed, that is, the direction of the region of interest is processed to obtain the processed region. In the processed region, the light and dark boundaries are vertical and the left side of the light and dark boundary is the dark area.

[0155] To determine the slope of the light and dark boundary, specifically, the grayscale value of each row of pixels in the processed region is extracted. The difference between the grayscale values ​​of two consecutive pixels is calculated using a difference equation to obtain the second difference curve. The specific formula can be found in the second embodiment and will not be elaborated here. The second difference curve is then filtered using a Hamming window to make it smoother, resulting in the third difference curve. In this embodiment, the Hamming window formula is:

[0156]

[0157] Where n = 1, 2, ..., n-1, n represents the total length of the window function, and M represents the effective length of the window function. In fact, the Hamming window is simply a cosine window with β = 0.54.

[0158] Find the first centroid of each third difference curve and perform linear regression on the first centroid to obtain the slope of the light-dark boundary: cent[y] = ky + b, where k can be used as the boundary slope of the light-dark boundary. In this embodiment, if the imaging device experiences imaging aberrations leading to distortion, a second fitting can be performed to make the obtained boundary slope more accurate.

[0159] The oversampling range is determined based on the boundary slope. The processed region is sampled four times to obtain the first difference curve. The first difference curve is then filtered using a Hamming window to obtain the filtered curve. A Fourier transform is performed on the filtered curve to obtain an axisymmetric transformed curve. Half of the transformed curve is normalized to obtain the normalized curve. The high-frequency portion of the normalized curve is amplified using the `correct` function to obtain the target curve. The target curve determines the modulation transfer function value, which is the ordinate of the spatial frequency response curve.

[0160] The sampling frequency of the grid test image obtained by the imaging device is calculated by dividing the full frequency by the pixel size of the imaging device. The horizontal axis step size is obtained by dividing the sampling frequency by the horizontal axis width of the target curve. The maximum horizontal coordinate is calculated by multiplying the horizontal axis width by the horizontal axis step size. The horizontal axis coordinate of the target curve is adjusted based on the horizontal axis step size and the maximum horizontal coordinate to obtain the spatial frequency response curve.

[0161] In addition, this invention also proposes an imaging capability evaluation device.

[0162] Please refer to Figure 10 The imaging capability evaluation device of this invention includes:

[0163] The imaging module 10 is used to capture a grid test image of a grid card through an imaging device, wherein the grid test image is distributed according to the field of view.

[0164] The processing module 20 is used to extract the region of interest corresponding to each field of view in the grid test image, and process the direction of the region of interest to obtain the processed region, wherein the light and dark boundary of the processed region is vertical and the left side of the light and dark boundary is a dark area.

[0165] Evaluation module 30 is used to calculate the spatial frequency response curve based on the light and dark boundary, and to evaluate the imaging capability of the imaging device in each field of view based on the spatial frequency response curve.

[0166] Optionally, the evaluation module 30 may include:

[0167] A calculation unit is used to calculate the linear regression line of the light and dark boundary;

[0168] A sampling unit is used to determine the sampling range for oversampling the processed region based on the boundary slope of the linear regression line, and to oversample the processed region according to the sampling range to obtain oversampled data;

[0169] The differential calculation unit is used to perform differential calculation on the oversampled data to obtain a first differential curve, and to process the first differential curve to obtain a target curve;

[0170] An adjustment unit is used to adjust the horizontal axis coordinate of the target curve to obtain a spatial frequency response curve.

[0171] Optionally, the computing unit may include:

[0172] The grayscale calculation subunit is used to determine the grayscale value of each pixel in the processed region and to calculate the grayscale difference between two adjacent pixels in each row.

[0173] The difference curve calculation subunit is used to obtain the second difference curve corresponding to each row of pixels based on the gray-level difference corresponding to each row of pixels;

[0174] The fitting subunit is used to calculate the first centroid of the second difference curve and to obtain the linear regression line of the light and dark boundary by linear fitting through each of the first centroids.

[0175] Optionally, the difference curve calculation subunit is also used for:

[0176] The second difference curve is subjected to Hamming window filtering to obtain the third difference curve, and the first centroid of the third difference curve is calculated.

[0177] Optionally, the evaluation module 30 may include:

[0178] The mapping subunit is used to determine the regression position of each of the first centroids mapped onto the linear regression line, and to perform Hamming window filtering on each of the second difference curves with each of the regression positions as the center to obtain the fourth difference curve.

[0179] The second-order fitting subunit determines the second centroid of the fourth difference curve and performs a second-order fitting based on each of the second centroids to obtain a second-order fitting line;

[0180] The first execution subunit is used to take the quadratic fitted line as the linear regression line and execute the step of determining the sampling range for oversampling the processed region based on the boundary slope of the linear regression line.

[0181] Optionally, the imaging capability evaluation device of the present invention further includes:

[0182] The detection module is used to detect whether imaging aberrations occur in the grid test image;

[0183] A computing unit may include:

[0184] The second execution subunit is used to perform the step of determining the regression position of each of the first centroids mapped on the linear regression line if imaging aberration occurs in the grid test image.

[0185] The third execution subunit is used to perform the step of determining the sampling range for oversampling the processed region based on the boundary slope of the linear regression line if no imaging aberration occurs in the grid test image.

[0186] Optionally, the difference calculation unit may include:

[0187] The filtering subunit is used to perform Hamming window filtering on the first difference curve with the third centroid of the first difference curve as the center to obtain the filtered curve.

[0188] The Fourier transform subunit is used to perform a Fourier transform on the filtered curve to obtain an axisymmetric transformed curve.

[0189] The normalization processing subunit is used to take half of the data of the transformed curve and perform normalization processing to obtain a normalized curve;

[0190] The amplification subunit is used to amplify the high-frequency portion of the normalized curve to obtain the target curve.

[0191] All embodiments of the imaging capability evaluation device of the present invention can refer to the various embodiments of the imaging capability evaluation method of the present invention, and will not be described again here.

[0192] Furthermore, embodiments of the present invention also propose a computer-readable storage medium storing an imaging capability evaluation program, which, when executed by a processor, implements the steps of the imaging capability evaluation method described below.

[0193] The various embodiments of the imaging capability evaluation device and computer-readable storage medium of the present invention can be referred to the various embodiments of the imaging capability evaluation method of the present invention, and will not be repeated here.

[0194] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0195] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0196] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0197] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for evaluating imaging capability, characterized in that, The imaging capability evaluation method includes the following steps: A grid test image is obtained by capturing a grid chart using an imaging device, wherein the grid test image is distributed according to the field of view; Extract the region of interest corresponding to each field of view in the grid test image, and process the direction of the region of interest to obtain the processed region, wherein the light and dark boundaries of the processed region are vertical and the left side of the light and dark boundary is the dark area. Calculate the linear regression line of the light and dark boundary; The sampling range for oversampling the processed region is determined based on the boundary slope of the linear regression line, and the processed region is oversampled according to the sampling range to obtain oversampled data. The oversampling process is as follows: the integer multiple of the reciprocal of the boundary slope is determined as the actual number of sampling rows for oversampling the processed region; the pixels of the preset number of rows are used as a group of pixels to sample the region to be sampled corresponding to the actual number of sampling rows to obtain oversampled data. The oversampled data is differentially calculated to obtain a first difference curve, and the first difference curve is processed to obtain a target curve; The spatial frequency response curve is obtained by adjusting the horizontal axis coordinate of the target curve; The imaging capability of the imaging device in each field of view is evaluated based on the spatial frequency response curve. The step of calculating the linear regression line of the light and dark boundary includes: Determine the grayscale value of each pixel in the processed region, and calculate the grayscale difference between two adjacent pixels in each row; The second difference curve for each row of pixels is obtained based on the grayscale difference value corresponding to each row of pixels; Calculate the first centroid of the second difference curve, and obtain the linear regression line of the light and dark boundary by linear fitting through each of the first centroids.

2. The imaging capability evaluation method as described in claim 1, characterized in that, The step of calculating the first centroid of the second difference curve includes: The second difference curve is subjected to Hamming window filtering to obtain the third difference curve, and the first centroid of the third difference curve is calculated.

3. The imaging capability evaluation method as described in claim 1, characterized in that, After the step of obtaining the linear regression line of the light and dark boundaries by linear fitting through each of the first centroids, the method further includes: Determine the regression position of each of the first centroids mapped onto the linear regression line, and perform Hamming window filtering on each of the second difference curves with each of the regression positions as the center to obtain the fourth difference curve; Determine the second centroid of the fourth difference curve, and perform a quadratic fitting based on each of the second centroids to obtain a quadratic fitted line; The quadratic fitted line is used as the linear regression line, and the step of determining the sampling range for oversampling the processed region based on the boundary slope of the linear regression line is performed.

4. The imaging capability evaluation method as described in claim 3, characterized in that, The imaging capability evaluation method also includes: Detect whether imaging aberrations occur in the grid test image; After the step of obtaining the linear regression line of the light and dark boundaries by linear fitting through each of the first centroids, the method further includes: If the grid test image has imaging aberrations, then the step of determining the regression position of each of the first centroids mapped onto the linear regression line is performed; If no imaging aberration occurs in the grid test image, then the step of determining the sampling range for oversampling the processed region based on the boundary slope of the linear regression line is performed.

5. The imaging capability evaluation method according to any one of claims 1 to 4, characterized in that, The step of processing the first difference curve to obtain the target curve includes: The first difference curve is filtered by applying a Hamming window filter to the first difference curve with the third centroid as the center. Performing a Fourier transform on the filtered curve yields an axisymmetric transformed curve. The normalized curve is obtained by normalizing half of the data from the transformed curve. The target curve is obtained by amplifying the high-frequency portion of the normalized curve.

6. An imaging capability evaluation device, characterized in that, The imaging capability evaluation device includes: The imaging module is used to capture a grid test image of a grid card using an imaging device, wherein the grid test image is distributed according to the field of view. The processing module is used to extract the region of interest corresponding to each field of view in the grid test image, and process the direction of the region of interest to obtain the processed region, wherein the light and dark boundary of the processed region is vertical and the left side of the light and dark boundary is a dark area. The evaluation module is used to calculate the spatial frequency response curve based on the light and dark boundaries, and to evaluate the imaging capability of the imaging device in each field of view based on the spatial frequency response curve. The evaluation module includes: A calculation unit is used to calculate the linear regression line of the light and dark boundary; A sampling unit is used to determine the sampling range for oversampling the processed region based on the boundary slope of the linear regression line, and to oversample the processed region according to the sampling range to obtain oversampled data. The oversampling process is as follows: the integer multiple of the reciprocal of the boundary slope is determined as the actual number of sampling rows for oversampling the processed region; and the region to be sampled corresponding to the actual number of sampling rows is sampled with pixels of a preset number of rows as a group of pixels to obtain oversampled data. The differential calculation unit is used to perform differential calculation on the oversampled data to obtain a first differential curve, and to process the first differential curve to obtain a target curve; An adjustment unit is used to adjust the horizontal axis coordinate of the target curve to obtain a spatial frequency response curve; The computing unit includes: The grayscale calculation subunit is used to determine the grayscale value of each pixel in the processed region and to calculate the grayscale difference between two adjacent pixels in each row. The difference curve calculation subunit is used to obtain the second difference curve corresponding to each row of pixels based on the gray-level difference corresponding to each row of pixels; The fitting subunit is used to calculate the first centroid of the second difference curve and to obtain the linear regression line of the light and dark boundary by linear fitting through each of the first centroids.

7. An imaging capability evaluation device, characterized in that, The imaging capability evaluation device includes: a memory, a processor, and an imaging capability evaluation program stored in the memory and executable on the processor. When the imaging capability evaluation program is executed by the processor, it implements the steps of the imaging capability evaluation method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an imaging capability evaluation program, which, when executed by a processor, implements the steps of the imaging capability evaluation method as described in any one of claims 1 to 5.

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