An image sensor defect correction method, system, and medium

Abstract

The application discloses a kind of image sensor flaw correction method, system and medium, correction method includes: fixed transparent target, it is located between light source and image sensor, and with image sensor each other parallel;Adjust target or image sensor to realize relative translation or rotation, and calculate the ratio of two pixel values in the two image data obtained by image sensor before and after adjustment corresponding to the same target position;When the ratio is beyond the preset threshold interval, the corresponding image sensor flaw position is screened;Calculate the correction coefficient of the flaw position relative to the normal position outside the flaw position.The application can accurately detect the newly added flaw of image sensor after leaving factory, only need to cooperate with transparent target to change relative pose, and the flaw position can be accurately positioned quickly, and the correction coefficient corresponding to the flaw position is analyzed quickly in combination with the data in detection process, to effectively improve detection efficiency.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of machine vision, and particularly relates to an image sensor flaw correction method, system and medium. BACKGROUND

[0002] Image sensors are widely used in various imaging devices, and the imaging quality directly affects the accuracy and reliability of the final image. However, image sensors are prone to introduce various flaws during the production process, packaging process or long-term use. These flaws mainly manifest in two categories: physical defects of the sensor semiconductor layer itself (such as bad pixels) and contamination, scratches or material degradation of the filter covering the surface of the sensor. A typical manifestation of these flaws is the reduction of the responsivity of the pixel points at specific locations of the sensor, i.e. the conversion efficiency of the pixel to the incident light signal is lower than the normal level, which is reflected in the collected image data as an abnormally low pixel value (dark) in the local area.

[0003] This local responsivity reduction seriously affects the imaging quality and may cause misjudgment or information loss in high-precision detection, scientific imaging, security monitoring and other scenarios. The main ways to solve this problem currently are divided into front-end calibration and back-end processing. Front-end calibration is usually performed in the factory, and bad pixel information is identified by shooting standard light sources or targets to generate a correction mapping table, but this can only solve the flaws existing at the time of delivery and cannot cope with new flaws generated during the use of the device due to environmental, aging and other factors. Back-end processing relies on complex image processing algorithms to try to identify and repair dark spots at the software level, however, such algorithms are highly dependent on scene content and are prone to misjudgment of real dark areas or complex textures in the image as flaws, resulting in unreliable detection results, requiring a large amount of computing resources and being difficult to achieve accurate detection and correction.

[0004] Therefore, the present application provides an image sensor flaw correction method, system and medium to accurately detect new flaws of image sensors after delivery and correct them. SUMMARY

[0005] The present application aims to overcome the above problems existing in the prior art and provides an image sensor flaw correction method, system and medium. By moving the target or the image sensor, image data before and after the movement of the image sensor is collected and aligned, and by using different sensor pixel data corresponding to the same physical position of the target, the specific flaw position can be analyzed by the larger or smaller ratio of the before and after data, thereby realizing flaw detection and correction.

[0006] To achieve the above technical purposes and effects, the present application realizes the following technical solutions:

[0007] The application discloses a method for correcting defects of an image sensor, which collects different image data by using the image sensor to confirm the defect position of the image sensor and correct the defect position, and the correction method comprises the following steps:

[0008] A transparent target is fixed between the light source and the image sensor and is parallel to the image sensor;

[0009] The target or the image sensor is adjusted to realize relative translation or rotation, and the ratio of two pixel values corresponding to the same target position in the two image data collected by the image sensor before and after the adjustment is calculated;

[0010] When the ratio exceeds a preset threshold interval, the corresponding image sensor defect position is screened out;

[0011] The correction coefficient of the defect position relative to the normal position outside the defect position is calculated.

[0012] Further, the ratio of two pixel values corresponding to the same target position in the two image data collected by the image sensor before and after the adjustment comprises the following steps:

[0013] The positions of the corresponding mark points in the two image data collected by the image sensor before and after the adjustment are confirmed;

[0014] The regions of interest corresponding to the two image data are extracted, and the regions of interest are bounded by the positions of the corresponding mark points;

[0015] The ratio of two pixel values corresponding to the same pixel position in the two regions of interest is calculated.

[0016] Further, when the ratio exceeds a preset threshold, the corresponding image sensor defect position comprises the following steps: the smaller value of the two pixel values is confirmed according to the ratio of the two pixel values, and the image sensor pixel position corresponding to the smaller value is marked as the image sensor defect position.

[0017] Further, the correction coefficient calculation method comprises the following steps:

[0018] The standard response rate of the image sensor is obtained to calculate the average transmittance of the target according to the actual pixel value of the normal position of the image sensor;

[0019] The actual pixel values of the defect position and the normal position of the image sensor are extracted to respectively calculate the actual response rates of the defect position and the normal position of the image sensor according to the average transmittance of the target;

[0020] The average value of the actual response rate of the normal position of the image sensor is calculated, so that the correction coefficient corresponding to the defect position of the image sensor is calculated according to the actual response rate of the defect position of the image sensor.

[0021] Further, the correction coefficient calculation method comprises: calculating a ratio of the average value of the actual pixel values of the normal position of the image sensor to the actual pixel value of the defective position of the image sensor as the correction coefficient corresponding to the defective position of the sensor.

[0022] Further, after the screening of the defective position of the image sensor is completed, the method further comprises:

[0023] When the ratio is determined to belong to a preset threshold interval, whether both of the two pixel values corresponding to the ratio are less than a preset reference threshold value:

[0024] If yes, the target or the image sensor is adjusted to realize relative translation or rotation, and if no, no response is made.

[0025] Further, the adjustment of the target or the image sensor to realize relative translation comprises: moving the target or the image sensor twice along the horizontal direction or the vertical direction, and the moving directions of the two times are opposite, so that all the pixel positions of the image sensor can be collected at least twice.

[0026] Further, if the image sensor is a color image sensor, the correction coefficients of each defective position of the image sensor are calculated separately in a channel-by-channel manner.

[0027] The application further provides an image sensor defect correction system, comprising:

[0028] a pose adjustment module, configured to adjust the target or the image sensor to realize relative translation or rotation; wherein the target is in a transparent state, located between the light source and the image sensor, and parallel to the image sensor;

[0029] an image analysis module, configured to calculate a ratio of two pixel values corresponding to the same target position in two image data collected by the image sensor before and after the adjustment;

[0030] a ratio analysis module, configured to screen the defective position of the image sensor corresponding to the ratio when the ratio exceeds a preset threshold interval;

[0031] a correction analysis module, configured to calculate a correction coefficient of the defective position relative to a normal position outside the defective position.

[0032] The application further provides a computer readable storage medium comprising a computer program, wherein the computer program is executed by a processor to realize the above-mentioned correction method.

[0033] The application has the following beneficial effects:

[0034] (1) The present application can accurately detect the newly added defects of the image sensor after leaving the factory, and only needs to change the relative pose with the transparent target to quickly and accurately position the defect position. Combined with the data in the detection process, the correction coefficient corresponding to the defect position can be quickly analyzed, without the need for additional image detection to obtain the correction target, and can be applied to the subsequent detection process after correction, without the need for real-time use of a large amount of computing resources, effectively improving the detection efficiency. The whole defect detection process is simple, efficient, and the correction result is accurate and reliable, effectively reducing the use cost and resource consumption.

[0035] (2) The transparent target is fixed between the light source and the image sensor, effectively solving the shielding problem caused by the traditional reflective target, avoiding the detection defects formed by the light source between the target and the image sensor or the image sensor between the target and the light source, and improving the subsequent detection accuracy in principle. By arranging the transparent target and the image sensor parallel to each other, the problems of uneven light intensity distribution and image distortion caused by angle inclination are effectively eliminated, other problem factors are avoided, and the reliability and accuracy of subsequent detection are ensured.

[0036] By changing the relative pose between the target and the image sensor, the same target position on the target can be captured by different pixels on the image sensor. In theory, if all pixels on the image sensor remain normal, the pixel data of the image sensor corresponding to any position on the target will remain the same. By analyzing the two image data captured by the image sensor before and after the change, the ratio of the two pixel values corresponding to the same target position in the two image data can be obtained, and whether the ratio result is abnormal can be quickly judged, so as to quickly lock the defect position on the image sensor.

[0037] By analyzing whether the ratio corresponding to all target positions belongs to the preset threshold interval, it can be accurately judged whether there is an abnormality at the two positions on the image sensor corresponding to the current target position. Since the relative pose between the target and the image sensor is changed in advance, the mapping relationship between any position of the target and the image information collected by the image sensor before and after the change is known. As long as the above ratio does not belong to the preset threshold interval, it can be quickly judged whether the smaller one is smaller or smaller based on the ratio result, and the image sensor position corresponding to the smaller one can be quickly locked based on the mapping relationship between all positions of the target and the image information collected by the image sensor, so as to determine the defect position of the image sensor.

[0038] By counting the actual pixel value of the image sensor flaw position and the normal position, the actual pixel value of the image sensor normal position can be used as the correction target. Considering the actual data floating, the minimum value or the maximum value or the average value of all the actual pixel values of the normal position can be used as the target value of the flaw correction. The corresponding actual pixel response rate can also be calculated from the actual pixel value of the image sensor normal position, so as to form a correction coefficient by dividing the corresponding data of the image sensor flaw position. In order to ensure the unity of the global data, the optimization coefficient of the normal area can also be calculated, so as to form the corresponding correction coefficient acting on the pixels of the image sensor normal area. BRIEF DESCRIPTION OF DRAWINGS

[0039] The drawings described herein are used to provide further understanding of the present application, and form a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation of the present application. In the drawings:

[0040] Figure 1 is a correction method flowchart in the present application;

[0041] Figure 2 is a correction system structure block diagram in the present application;

[0042] Figure 3 is a correction device structure schematic diagram in the present application.

[0043] In the drawings: 1-image sensor; 2-filter; 3-light source; 4-target. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0045] The image sensor has physical defects (such as bad points) or the pollution, scratch or material degradation of the filter (for example, Bayer filter) covered on the sensor surface, which causes the Responsivity of the pixel points at specific positions of the sensor to decrease, that is, the conversion efficiency of the pixel to the incident light signal is lower than the normal level, which is reflected on the collected image data as the abnormal low pixel value (dark) in the local area.

[0046] In order to accurately detect the newly added flaw of the sensor after leaving the factory and correct it, such as Figure 1As shown, the embodiment first provides a kind of image sensor flaw correction method, different image data are collected using image sensor, to confirm image sensor flaw position and carry out correction, the correction method includes:

[0047] Fix transparent target, it is located between light source and image sensor, and it is parallel with image sensor.

[0048] The target is transparent material, its transmissivity is uniform, and a plurality of mark points are distributed on the surface, for example, four cross-shaped marks are arranged in the central region in rectangular distribution, to facilitate fast positioning when processing image data collected by image sensor later.Light source uses uniform and constant standard light source, and its illumination intensity is known.Because subsequent process involves image sensor detection of image information on the surface of target, if traditional reflective target is used, it will face installation contradiction of light source and image sensor, if light source is between target and image sensor, light source blocks the detection of image sensor, if image sensor is between target and light source, the blocking projection formed by image sensor seriously affects subsequent detection accuracy, therefore, by fixing transparent target between light source and image sensor, the blocking problem caused by traditional reflective target is effectively solved, and the detection defects formed by light source between target and image sensor or image sensor between target and light source are avoided, which improves subsequent detection accuracy in principle.Through parallel arrangement of transparent target and image sensor, problems such as uneven light intensity distribution and image distortion caused by angle inclination are effectively eliminated, other problem factors are avoided, and reliability and accuracy of subsequent detection are ensured.

[0049] Adjust target or image sensor to realize relative translation or rotation, and calculate the ratio of two pixel values corresponding to the same target position in two image data acquired by image sensor before and after adjustment.

[0050] Because target and image sensor are arranged in parallel, whether target or image sensor is moved, the result collected by image sensor is consistent, and by changing the relative pose between target and image sensor, the same target position on target can be collected by pixels at different positions on image sensor, in theory, if all pixels at different positions on image sensor remain normal, then pixel data of image sensor at two different positions corresponding to any position on target will remain consistent, and by analyzing two image data captured by image sensor before and after change, the ratio of two pixel values corresponding to the same target position in two image data can be obtained, whether the ratio result is abnormal can be quickly judged, so as to quickly lock the flaw position on image sensor.

[0051] When the ratio exceeds the preset threshold interval, the corresponding image sensor defect position is screened. That is, whether the ratio corresponding to all target positions belongs to the preset threshold interval is analyzed, so as to determine the corresponding image sensor defect position according to the ratio exceeding the preset threshold interval.

[0052] As can be seen from the above, the ratio corresponding to any position on the target is close to 1 under normal conditions of the image sensor. Considering the actual measurement error and the like, the preset threshold interval such as (0.98, 1.02) can be set according to experience. By analyzing whether the ratio corresponding to all target positions belongs to the preset threshold interval, it can be accurately determined whether there is an abnormality at the two positions on the image sensor corresponding to the current target position. Since the relative pose between the target and the image sensor before and after the change is known in advance, the mapping relationship between any position of the target before and after the change and the image information collected by the image sensor is known. As long as the above ratio does not belong to the preset threshold interval, it can be quickly judged whether the numerator or the denominator is smaller according to the ratio result, and the image sensor position corresponding to the smaller one can be quickly locked based on the mapping relationship between all positions of the target and the image information collected by the image sensor, so that the image sensor defect position is determined.

[0053] The correction coefficient of the defect position relative to the normal position outside the defect position is calculated, that is, the actual pixel values of the image sensor defect position and the normal position are counted, so that the correction coefficient of each defect position of the image sensor is calculated according to the actual pixel value of the normal position of the image sensor.

[0054] Since the defect position of the image sensor has been determined at this time, the actual pixel value of the normal position of the image sensor can be taken as the target of correction by counting the actual pixel values of the image sensor defect position and the normal position. Considering the actual data floating, the minimum value, the maximum value or the average value of all actual pixel values of the normal position can be taken as the target value of defect correction, or the corresponding actual pixel response rate can be calculated from the actual pixel value of the normal position of the image sensor, so as to form a correction coefficient by dividing the corresponding data of the image sensor defect position. In order to ensure the unity of global data, the optimization coefficient of the normal area can also be calculated in the same way, so as to form the corresponding correction coefficient acting on the pixels in the normal area of the image sensor.

[0055] In order to accurately obtain the ratio of the two pixel values corresponding to the same target position in the two image data before and after the change, the ratio of the two pixel values corresponding to the same target position in the two image data obtained by the image sensor before and after the adjustment is calculated, including:

[0056] The corresponding mark point positions in the two image data captured by the image sensor before and after the adjustment are determined.

[0057] The regions of interest corresponding to the two image data are extracted, and the regions of interest are bounded by the corresponding marker point positions.

[0058] The ratio of the two pixel values corresponding to the same pixel position in the two regions of interest is calculated.

[0059] As can be seen from the above, the target or image sensor is adjusted to achieve relative translation or rotation. When translation is selected, that is, the target or image sensor is moved, the movement amount in the x direction is Δx, and the movement amount in the y direction is Δy. For the two image data captured by the image sensor before and after the change, the corresponding marker point positions can be found by using mature image processing techniques such as the center of mass calculation algorithm, for example, the four corner points of a rectangular region. By extracting the regions of interest (ROIs) corresponding to the two image data, two ROI images of the same size are formed, denoted as image I1 and image I2. The pixel values of the two images at position (i, j) are I1(i, j) and I2(i, j), respectively. For any coordinate position (i, j) in the ROI image, the sensor coordinate position before translation is (i-Δx, j-Δy). The ratio ρ(i, j) of I1(i, j) and I2(i, j) is calculated as follows:

[0060]

[0061] where T(i, j) represents the transmittance of the corresponding target position (i, j) (which is a constant in theory under normal conditions); L represents the light source intensity (which is usually uniform and known); S(i, j) represents the response rate of the sensor position (i, j), and S(i-Δx, j-Δy) represents the response rate of the sensor position (i-Δx, j-Δy). In theory, S(i, j) and S(i-Δx, j-Δy) should be the same constant.

[0062] For a normal region, if T(i, j) and L are uniform, then ρ(i, j) ≈1, and the specific analysis is as follows:

[0063] If threshold_low≤ ρ(i, j) ≤ threshold_high: indicates that the corresponding sensor pixel at the current position is normal, where threshold_low and threshold_high are the lower limit value and the upper limit value of the preset threshold range, respectively. threshold_low and threshold_high can be set based on empirical values, such as the mean and standard deviation of the normal region.

[0064] If ρ(i, j) > threshold_high: the denominator S(i-Δx, j-Δy) is small, indicating that the sensor position (i-Δx, j-Δy) has a flaw (low transmittance).

[0065] If ρ(i, j) < threshold_low: the numerator S(i, j) is small, indicating that the sensor position (i, j) has a flaw (low transmittance).

[0066] From the above, adjusting the target or image sensor to achieve relative translation or rotation, when selecting rotation, that is, rotating the target or image sensor, taking the rotating image sensor as an example, assuming that the rotation angle is θ, and the rotation center is (p i , p j ), for any coordinate position (i, j) in the ROI image, the coordinate position (i', j') of the corresponding sensor before rotation is calculated as follows:

[0067]

[0068] From the above, by calculating the ratio ρ(i, j) of I1(i, j) and I2(i, j), the specific method is as follows:

[0069]

[0070] Thus we get:

[0071]

[0072] Where S(p i +(i-p i )cosθ+(j-p j )sinθ,p j -(i-p i )sinθ+(j-p j )cosθ) represents the response rate of the sensor position (p i +(i-p i )cosθ+(j-p j )sinθ,p j -(i-p i )sinθ+(j-p j )cosθ).

[0073] For the specific method of obtaining the ratio ρ(i, j) by rotation, considering the rotation center (p i , p j)The corresponding ratio ρ(i, j) must be equal to 1, a rotation can be added as a complementary detection of this position, the specific analysis method can refer to the above translation process, as an extended embodiment of the present application, rotation and translation can also be combined, which will not be described here.

[0074] In order to correct the defect position of the image sensor subsequently, when the ratio exceeds the preset threshold value, the corresponding image sensor defect position is confirmed according to the ratio of the two pixel values, and the smaller value is marked as the image sensor pixel position, so as to serve as the image sensor defect position.

[0075] As a specific embodiment of the present application, if the position (i, j) has a defect, M(i, j) =1, otherwise M(i, j) =0, as follows:

[0076] If ρ(i, j) > threshold_high: M(i-Δx, j-Δy) =1 (the defect is in the sensor position (i-Δx, j-Δy) ).

[0077] If ρ(i, j) < threshold_low: M(i, j) =1 (the defect is in the sensor position (i, j) ).

[0078] As can be seen from the above, a typical manifestation of the image sensor defect is the decrease of the response rate of the pixel point, that is, the conversion efficiency of the pixel to the incident light signal is lower than the normal level, which is reflected in the image data collected as the abnormal low (dark) pixel value of the local area, therefore, the correction coefficient calculation method comprises:

[0079] Obtain the standard response rate S of the image sensor ref , to calculate the average transmittance T of the target according to the actual pixel value of the normal position of the image sensor avg . The light source intensity is L, usually S ref and L can be known in advance, the target transmittance corresponding to each pixel position of the normal area of the image sensor is I1(i, j) / LS ref , and the average transmittance T of the target can be obtained by accumulating and averaging the transmittance of each pixel position of the normal area. avg

[0080] Extract the actual pixel value I1(i, j) of the defect position and the normal position of the image sensor, to calculate the actual response rate of the defect position and the normal position of the image sensor according to the average transmittance T of the target avg , the actual response rate of the defect position and the normal position of the image sensor can be calculated by I1(i, j) / LT avg . ​

[0081] statistical average value S of actual response rates of normal positions of the image sensor normal , thereby calculating the actual response rate S of the image sensor at the defective position according to the image real (i, j) to calculate the corresponding correction coefficient a(i, j), where a(i, j) = S normal / S real (i, j).

[0082] To simplify the algorithm, as another embodiment of the present application, the correction coefficient calculation method comprises: calculating the average value of actual pixel values of normal positions of the image sensor, and calculating the ratio of the average value to actual pixel values of defective positions of the image sensor as the correction coefficient corresponding to the sensor defective position. This way can effectively utilize the image data collected during movement, and realize efficient correction of defect confirmation.

[0083] As can be seen from the above, if the defective area of the image sensor is large, I1(i, j) and I2(i, j) of some pixel positions in the ROI image may be abnormal but the ratio ρ(i, j) is normal after moving the target or the image sensor. For this case, after the image sensor defective position screening is completed, it further comprises:

[0084] determining whether the two pixel values corresponding to the ratio belong to a preset threshold interval:

[0085] If yes, adjust the target or the image sensor to realize relative translation or rotation, such as increasing the movement amount Δx in the x direction and the movement amount Δy in the y direction, and if no, do nothing.

[0086] The preset reference threshold can be set by referring to the global pixel average value, for example, the preset reference threshold is 0.8 times the global pixel average value of the ROI image, and the specific multiple can be flexibly adjusted according to actual experience value, wherein the global pixel average value I global_avg The calculation formula is as follows:

[0087]

[0088] Wherein, N is the total number of pixels in the ROI image.

[0089] Through the above-mentioned manner, the movement amount in the x direction and the movement amount in the y direction of the target or the image sensor after movement can be made to exceed the span of the sensor defect in the translation direction, that is, to avoid the cross region of the defect area after translation, and to ensure the reliability and accuracy of the detection result.

[0090] In order to detect and correct the defects of all pixel positions of the image sensor, the relative translation of the target or the image sensor comprises: moving the target or the image sensor twice in the lateral direction or the longitudinal direction, and the moving directions are opposite, so that all pixel positions of the image sensor can capture the target at least twice. Taking the lateral movement as an example, the sizes of the two ROI images extracted after moving once are necessarily smaller than the size of the image sensor, even if the height of the ROI image is equal to the height of the image sensor in the ideal case, the ROI image obtained by moving once in the opposite direction can still completely cover all areas of the image sensor, so that all positions of the image sensor can be detected and corrected.

[0091] As a specific embodiment of the present application, if the image sensor is a color image sensor, the correction coefficients of each defect position of the image sensor are calculated separately in different channels. Taking a surface array image sensor with a Bayer array as an example, the RGB data of each pixel position of the image sensor is calculated in advance by interpolation, and for the R, G and B channels, the corresponding correction coefficients a(i, j)_R, a(i, j)_G and a(i, j)_B are calculated respectively according to the above correction method, so that each pixel position of the image sensor is corrected separately in different channels.

[0092] As shown in Figure 2 The second aspect of the present application further provides an image sensor defect correction system, comprising:

[0093] A pose adjustment module is configured to adjust the target or the image sensor to realize relative translation or rotation; wherein the target is in a transparent state, is located between the light source and the image sensor, and is parallel to the image sensor.

[0094] An image analysis module is configured to calculate the ratio of two pixel values corresponding to the same target position in the two image data obtained by the image sensor before and after adjustment.

[0095] A ratio analysis module is configured to screen the image sensor defect position corresponding to the ratio that exceeds the preset threshold interval.

[0096] A correction analysis module is configured to calculate the correction coefficient of the defect position relative to the normal position outside the defect position.

[0097] The specific implementation method of the correction system can be operated by referring to the above correction method, which will not be described here.

[0098] As shown in Figure 3 The third aspect of the present application further provides an image sensor defect correction device, comprising:

[0099] An image sensor 1 is connected to the image analysis module and is configured to acquire image information.​​

[0100] Filter 2, distributed in front of the sensing area of the image sensor 1, is a component that moves synchronously with the image sensor 1, and generally, the pollution of the filter 2 or the defects of the image sensor 1 can cause the acquisition result of the image sensor 1 to be abnormal.

[0101] Light source 3, the illumination direction is opposite to the sensing area of the image sensor 1, is used for providing uniform and constant illumination, and the illumination intensity is known.

[0102] Target 4, made of transparent material, has uniform transmittance, is located between the light source 3 and the image sensor 1, and is parallel to the image sensor 1, and is a component that moves synchronously with the light source 3, and four cross-shaped marks in a rectangular distribution can be arranged in the central area, which facilitates fast positioning when the image data collected by the image sensor 1 is processed later.

[0103] Based on the correction device, the flaw detection and correction of the image sensor 1 or the filter 2 are realized, and the specific operation method can be referred to the above-mentioned correction method for operation, which will not be repeated here.

[0104] The fourth aspect of the present application also provides a computer readable storage medium comprising a computer program, which is executed by a processor to realize the above-mentioned correction method.

[0105] In practical applications, the computer readable storage medium can adopt any combination of one or more computer readable media. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device or component, or any combination of the above. More specific examples of the computer readable storage medium can include an electrical connection having one or more conductive wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present embodiment, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component.

[0106] A computer readable signal medium can include a propagated data signal with computer executable code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal can take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium can be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate or transport programming code.

[0107] Program code embodied on a computer readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wire line, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0108] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In an embodiment, electronic program guide data can be downloaded to the user's computer from an Internet data site, for example, using a Web browser.

[0109] In the description of the specification, the description of the terms "one embodiment", "an example", "a specific example" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Also, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in one or more embodiments or examples.

[0110] The above has shown and described the basic principles, main features and advantages of the present application. It should be understood by those skilled in the art that the present application is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only illustrative of the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application.

Claims

1. A method for correcting defects in an image sensor, comprising using an image sensor to acquire different image data to identify and correct the location of defects in the image sensor, characterized in that, The correction method includes: A fixed transparent target is positioned between the light source and the image sensor, and parallel to the image sensor. Adjust the target or image sensor to achieve relative translation or rotation, and calculate the ratio of two pixel values ​​corresponding to the same target position in two image data acquired by the image sensor before and after the adjustment; When the ratio exceeds a preset threshold range, the corresponding image sensor defect locations are selected. Calculate the correction coefficient of the defect location relative to a normal location outside the defect location.

2. The image sensor defect correction method according to claim 1, characterized in that, The calculation of the ratio of two pixel values ​​corresponding to the same target position in two image data sets acquired by the image sensor before and after adjustment includes: Confirm the positions of the marker points in the two image data captured by the image sensor before and after the adjustment; Extract the region of interest corresponding to the two image data, with the corresponding marker point position as the boundary of the region of interest; Calculate the ratio of the two pixel values ​​corresponding to the same pixel position in two regions of interest.

3. The image sensor defect correction method according to claim 2, characterized in that, When the ratio exceeds a preset threshold, the corresponding image sensor defect location is determined by: identifying the smaller of the two pixel values ​​based on the ratio of the two pixel values, and marking the image sensor pixel location corresponding to the smaller value as the image sensor defect location.

4. The image sensor defect correction method according to claim 3, characterized in that, The method for calculating the correction coefficient includes: Obtain the standard response rate of the image sensor to calculate the average transmittance of the target based on the actual pixel values ​​at the normal position of the image sensor. The actual pixel values ​​of the defective and normal locations of the image sensor are extracted, and the actual response rates of the defective and normal locations of the image sensor are calculated based on the average transmittance of the target. The average actual response rate of the image sensor at normal locations is statistically analyzed, and the corresponding correction coefficient is calculated based on the actual response rate at defective locations of the image sensor.

5. The image sensor defect correction method according to claim 3, characterized in that, The method for calculating the correction coefficient includes: calculating the average value of the actual pixel values ​​at normal positions of the image sensor, and then calculating the ratio of this average value to the actual pixel values ​​at defective positions of the image sensor, which is used as the correction coefficient corresponding to the defective position of the sensor.

6. A method for correcting image sensor defects according to claim 4 or 5, characterized in that, After the image sensor defect location screening is completed, the following steps are also included: When the ratio falls within a preset threshold range, determine whether both corresponding pixel values ​​are less than a preset reference threshold: If yes, the target or image sensor is adjusted to achieve relative translation or rotation; otherwise, no response is made.

7. The image sensor defect correction method according to claim 6, characterized in that, Adjusting the target or image sensor to achieve relative translation includes moving the target or image sensor twice in the horizontal or vertical direction, with the two movements in opposite directions, so that all pixel positions of the image sensor can be captured from the target at least twice.

8. The image sensor defect correction method according to claim 7, characterized in that, If the image sensor is a color image sensor, the correction coefficients for each defect location of the image sensor are calculated separately for each channel.

9. An image sensor defect correction system, characterized in that, include: The pose adjustment module is used to adjust the target or image sensor to achieve relative translation or rotation; wherein the target is transparent, located between the light source and the image sensor, and is parallel to the image sensor. The image analysis module is used to calculate the ratio of two pixel values ​​corresponding to the same target position in two image data acquired by the image sensor before and after adjustment; The ratio analysis module is used to filter the image sensor defect locations corresponding to ratios that exceed a preset threshold range. The correction analysis module is used to calculate the correction coefficient of the defect location relative to a normal location outside the defect location.

10. A computer-readable storage medium comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the correction method as described in any one of claims 1-8.

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