Imaging system parameter determination method and device

Abstract

The invention discloses an imaging system parameter determination method and device, and belongs to the technical field of industrial detection. The imaging system parameter determination method comprises the following steps: acquiring a plurality of first images corresponding to a to-be-detected object; fusing the at least two first images under the same group of imaging system parameters by adopting at least one fusion algorithm to obtain candidate fusion images corresponding to each fusion algorithm under each group of imaging system parameters; screening the plurality of candidate fusion images based on the image quality parameters corresponding to the candidate fusion images to obtain a target fusion image corresponding to the to-be-detected object; and adjusting the imaging system based on the imaging system parameters corresponding to the target fusion image and a fusion algorithm. The imaging system parameter determination method does not need to consume a large amount of labor cost and time cost to manually adjust the parameters of the imaging system and the fusion mode, and the industrial detection efficiency is improved.

Description

Technical Field

[0001] The present application belongs to the field of industrial detection technology, and in particular, relates to a method and device for determining parameters of an imaging system. Background Art

[0002] In industrial quality inspection imaging, due to factors such as surface material, light source or focal length, the imaging may be overexposed and blurred, affecting stable detection. In related technologies, there is a method of obtaining high-quality images through computational imaging, but this method requires deployment personnel to constantly adjust imaging system parameters and fusion methods in order to obtain the best image, which requires a lot of manpower costs, is time-consuming and inefficient. Summary of the invention

[0003] The present application aims to solve at least one of the technical problems existing in the related art. To this end, the present application proposes a method and device for determining imaging system parameters, which does not require a lot of manpower and time costs to manually adjust the parameters and fusion methods of the imaging system, thereby improving the efficiency of industrial inspection.

[0004] In a first aspect, the present application provides a method for determining parameters of an imaging system, the method comprising:

[0005] Acquire a plurality of first images corresponding to the object to be measured, wherein the plurality of first images include a plurality of first images acquired by the imaging system under a plurality of sets of imaging system parameters, and the same set of imaging system parameters corresponds to at least two first images under different stroboscopic regions;

[0006] Using at least one fusion algorithm to perform fusion processing on at least two first images under the same group of imaging system parameters, respectively, to obtain candidate fusion images corresponding to each fusion algorithm under each group of imaging system parameters;

[0007] Screening and processing the plurality of candidate fused images based on the image quality parameters corresponding to the candidate fused images to obtain a target fused image corresponding to the object to be measured;

[0008] The imaging system is adjusted based on imaging system parameters and a fusion algorithm corresponding to the target fused image.

[0009] According to the imaging system parameter determination method provided in the embodiment of the present application, a plurality of imaging system parameter combinations are used to acquire images of the object to be measured, and at least one fusion algorithm is used to fuse multiple first images under the same set of imaging system parameters, so that multiple candidate fused images can be obtained, so as to automatically infer a target fused image with better quality based on the image quality of each candidate fused image, so that deployment personnel can deploy the imaging system based on the imaging system parameters and the fusion algorithm of the target fused image, so as to obtain high-quality images based on the imaging system, without consuming a lot of manpower and time costs to manually adjust the parameters of the imaging system and the fusion method, thereby improving industrial inspection efficiency.

[0010] In an imaging system parameter determination method according to an embodiment of the present application, the method of screening and processing a plurality of candidate fused images based on the image quality parameters corresponding to each of the candidate fused images to obtain a target fused image corresponding to the object to be measured includes:

[0011] Based on at least one of the clarity, exposure, image quality and saturation corresponding to each of the candidate fused images, obtaining a target score corresponding to each of the candidate fused images;

[0012] Based on the target scores corresponding to the candidate fused images, at least one candidate fused image among the plurality of candidate fused images whose target scores are greater than a target threshold is determined as the target fused image.

[0013] In an imaging system parameter determination method according to an embodiment of the present application, obtaining a target score corresponding to each candidate fused image based on at least one of clarity, exposure, image quality and saturation corresponding to each candidate fused image includes:

[0014] Based on the clarity corresponding to each of the candidate fused images, a clarity score corresponding to each of the candidate fused images is calculated, and a plurality of candidate fused images whose clarity scores are greater than a first threshold among the plurality of candidate fused images are determined as a first image set;

[0015] Based on the exposure corresponding to each candidate fused image in the first image set, calculate the exposure score corresponding to each candidate fused image, and determine a plurality of candidate fused images in the first image set whose exposure score is greater than a second threshold as the second image set;

[0016] Based on the image quality and saturation corresponding to each of the candidate fused images in the second image set, the target score corresponding to each of the candidate fused images is calculated.

[0017] In an imaging system parameter determination method according to an embodiment of the present application, the target score corresponding to each candidate fused image is calculated based on the image quality and saturation corresponding to each candidate fused image in the second image set, including:

[0018] Based on the image quality corresponding to each of the candidate fused images in the second image set, an image quality score corresponding to each of the candidate fused images is calculated, and a plurality of candidate fused images in the second image set whose image quality scores are greater than a third threshold are determined as a third image set;

[0019] sorting the plurality of candidate fused images in the third image set in a target order based on the image quality scores, and assigning a new score to each sorted candidate fused image to obtain a first standardized score corresponding to each sorted candidate fused image;

[0020] Based on the saturation corresponding to each of the candidate fused images in the third image set, calculating a saturation score corresponding to each of the candidate fused images;

[0021] sorting the plurality of candidate fused images in the third image set in the target order based on the saturation score, and assigning a new score to each sorted candidate fused image to obtain a second standardized score corresponding to each sorted candidate fused image;

[0022] The sum of the first standardized score and the second standardized score corresponding to each candidate fused image in the third image set is determined as the target score corresponding to the candidate fused image.

[0023] In an imaging system parameter determination method according to an embodiment of the present application, the method of using at least one fusion algorithm to fuse at least two first images under the same set of imaging system parameters to obtain candidate fused images corresponding to each fusion algorithm under each set of imaging system parameters includes:

[0024] Performing channel separation processing on at least two first images under the same set of imaging system parameters to obtain second images corresponding to each channel under each stroboscopic area;

[0025] Based on the pixel value at the same pixel position in the second image corresponding to each stroboscopic area under the same channel, a third image under the same channel is generated;

[0026] The third images under each of the channels are fused to obtain a candidate fused image corresponding to the object to be measured.

[0027] In an imaging system parameter determination method according to an embodiment of the present application, the method of using at least one fusion algorithm to fuse at least two first images under the same set of imaging system parameters to obtain candidate fused images corresponding to each fusion algorithm under each set of imaging system parameters includes:

[0028] Based on the weight coefficients corresponding to the first images under the same set of imaging system parameters, weighted fusion processing is performed on the at least two first images under the same set of imaging system parameters to obtain the candidate fused image.

[0029] In a second aspect, the present application provides an imaging system parameter determination device, the device comprising:

[0030] A first processing module is used to obtain a plurality of first images corresponding to the object to be measured, wherein the plurality of first images include a plurality of first images acquired by the imaging system under a plurality of sets of imaging system parameters, and the same set of imaging system parameters corresponds to at least two first images under different stroboscopic areas;

[0031] A second processing module, configured to use at least one fusion algorithm to perform fusion processing on at least two first images under the same group of imaging system parameters, to obtain candidate fusion images corresponding to each fusion algorithm under each group of imaging system parameters;

[0032] A third processing module, configured to screen and process the plurality of candidate fused images based on the image quality parameters corresponding to the candidate fused images, to obtain a target fused image corresponding to the object to be measured;

[0033] The fourth processing module is used to adjust the imaging system based on the imaging system parameters and fusion algorithm corresponding to the target fused image.

[0034] According to the imaging system parameter determination device provided in the embodiment of the present application, a plurality of imaging system parameter combinations are used to acquire images of the object to be measured, and at least one fusion algorithm is used to fuse multiple first images under the same set of imaging system parameters, so that multiple candidate fused images can be obtained, so as to automatically infer a target fused image with better quality based on the image quality of each candidate fused image, so that deployment personnel can deploy the imaging system based on the imaging system parameters and the fusion algorithm of the target fused image, so as to obtain high-quality images based on the imaging system, without consuming a lot of manpower and time costs to manually adjust the parameters of the imaging system and the fusion method, thereby improving industrial inspection efficiency.

[0035] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for determining imaging system parameters as described in the first aspect above is implemented.

[0036] In a fourth aspect, the present application provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the imaging system parameter determination method as described in the first aspect above is implemented.

[0037] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the imaging system parameter determination method as described in the first aspect above.

[0038] The above one or more technical solutions in the embodiments of the present application have at least one of the following technical effects:

[0039] By adopting a variety of imaging system parameter combinations to collect images of the object to be measured, and adopting at least one fusion algorithm to fuse multiple first images under the same group of imaging system parameters, multiple candidate fusion images can be obtained, so as to automatically infer a target fusion image with better quality based on the image quality of each candidate fusion image, so that deployment personnel can deploy the imaging system based on the imaging system parameters and fusion algorithm of the target fusion image, so as to obtain high-quality images based on the imaging system, without consuming a lot of manpower and time costs to manually adjust the parameters of the imaging system and the fusion method, thereby improving the efficiency of industrial inspection.

[0040] Furthermore, by performing image screening based on the clarity and exposure of each candidate fused image, higher quality images can be screened out, and then the higher quality images are precisely sorted using the image quality score, and the saturation is used for sorting correction, so that a target fused image with a higher overexposure suppression effect can be obtained for deployment personnel to adjust the imaging system, so that the imaging system after adjusting the parameters can generate an image with a better de-overexposure effect.

[0041] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0043] Figure 1 is a flow chart of a method for determining imaging system parameters provided in an embodiment of the present application;

[0044] Figure 2 This is one of the principle schematic diagrams of the imaging system parameter determination method provided in the embodiment of the present application;

[0045] Figure 3 This is the second principle schematic diagram of the imaging system parameter determination method provided in the embodiment of the present application;

[0046] Figure 4 is a structural schematic diagram of an imaging system parameter device provided in an embodiment of the present application;

[0047] Figure 5 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0049] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0050] In conjunction with the accompanying drawings, the imaging system parameter determination method, imaging system parameter determination device, electronic device and readable storage medium provided in the embodiments of the present application are described in detail through specific embodiments and their application scenarios.

[0051] The imaging system parameter determination method may be applied to a terminal, and may be specifically executed by hardware or software in the terminal.

[0052] The terminal includes, but is not limited to, a portable communication device such as a mobile phone or tablet computer with a touch-sensitive surface (e.g., a touch screen display and / or a touch pad). It should also be understood that in some embodiments, the terminal may not be a portable communication device, but a desktop computer with a touch-sensitive surface (e.g., a touch screen display and / or a touch pad).

[0053] In the following various embodiments, a terminal including a display and a touch-sensitive surface is described. However, it should be understood that the terminal may include one or more other physical user interface devices such as a physical keyboard, a mouse and a joystick.

[0054] The imaging system parameter determination method provided in the embodiment of the present application may be executed by an electronic device or a functional module or functional entity in the electronic device that can implement the imaging system parameter determination method. The electronic devices mentioned in the embodiment of the present application include but are not limited to mobile phones, tablet computers, computers, cameras, and wearable devices. The imaging system parameter determination method provided in the embodiment of the present application is described below using an electronic device as an example of an execution subject.

[0055] like Figure 1 As shown, the imaging system parameter determination method includes: step 110, step 120, step 130 and step 140.

[0056] Step 110: Acquire a plurality of first images corresponding to the object to be measured;

[0057] In this step, the imaging system parameter determination method can be applied to industrial inspection scenarios, and the objects to be inspected may include industrial equipment and products, etc. For example, the objects to be inspected may include mechanical equipment, automation equipment, and metal materials, etc.

[0058] The multiple first images include images of the object to be measured acquired by the imaging system under multiple sets of imaging system parameters.

[0059] The imaging system parameters may include exposure parameters, focal length parameters, distances from the sub-zone stroboscopic light source to the object to be measured, and imaging system gain parameters.

[0060] Each parameter can be adjusted to different values. By arranging and combining the parameters, multiple sets of imaging system parameters can be obtained.

[0061] For example, suppose the exposure parameters can be adjusted to a1, a2, ..., a Na , the focal length parameters can be adjusted to b1, b2, ...., b Nb , the distance from the partitioned stroboscopic light source to the object to be measured can be adjusted to c1, c2, ..., c Nc , the imaging system gain parameters can be adjusted to d1, d2, ...., d Nd , other parameters can also be adjusted to multiple different values, which will not be described in detail in this application.

[0062] The imaging system can automatically arrange and combine these parameters to obtain *... combinations of imaging system parameters.

[0063] The same set of imaging system parameters corresponds to at least two first images in different stroboscopic areas.

[0064] Different stroboscopic areas have different lighting conditions, and the exposure areas of the images of the object to be measured collected in different stroboscopic areas are also different.

[0065] For example, two sets of imaging system parameters (a and b) can be set to capture images of the object to be measured in two stroboscopic areas (area one and area two) to obtain multiple first images; under the imaging system parameters a, image capture can be performed on the object to be measured in area one and area two respectively to obtain two first images; and under the imaging system parameters b, image capture can be performed on the object to be measured in area one and area two respectively to obtain two first images.

[0066] In some embodiments, before step 110, the imaging system parameter determination method may further include:

[0067] The imaging system is controlled to collect images of the object to be measured at least twice in different stroboscopic areas.

[0068] In this embodiment, if Figure 2 As shown, the imaging system may include an image acquisition device, for example, the imaging system may include a camera.

[0069] The camera can be placed above the object to be measured.

[0070] The camera's field of view covers the object to be measured.

[0071] The irradiation direction of the light source in each stroboscopic area is toward the object to be measured.

[0072] In one stroboscopic region, at least two image acquisitions may be performed on the object to be measured, and the acquired images correspond to the same overexposed region, but the imaging system parameters corresponding to the images are different.

[0073] The zone strobe can be divided into two zones, three zones or four zones, such as Figure 2 An example of a lighting structure with three-zone stroboscopic lighting is given, and the light source plane can be divided into four zones (zone one, zone two and zone three). The lights in zone one can be lit in sequence, while the lights in other zones are kept dark, and image acquisition is performed to obtain Img1; the lights in zone two can be lit, while the lights in other zones are kept dark, and image acquisition is performed to obtain Img2; the lights in zone three can be lit, while the lights in other zones are kept dark, and image acquisition is performed to obtain Img3; when light sources in different stroboscopic zones illuminate the object to be measured, the overexposed areas of the first image acquired are different.

[0074] Step 120: using at least one fusion algorithm to perform fusion processing on at least two first images under the same set of imaging system parameters, respectively, to obtain candidate fusion images corresponding to each fusion algorithm under each set of imaging system parameters;

[0075] In this step, at least one fusion algorithm includes a minimization fusion algorithm, a weighted fusion algorithm, a pyramid fusion algorithm, a linear fusion algorithm, and a deep learning-based fusion algorithm, etc., which can be selected based on user needs and are not limited in this application.

[0076] For multiple first images acquired under the same set of imaging system parameters, different fusion algorithms may be used to perform fusion processing on the multiple first images to obtain candidate fusion images corresponding to different fusion algorithms.

[0077] For example, three first images, Img1, Img2 and Img3, are collected under imaging system parameters a. Then, the multiple first images can be fused using b fusion algorithm and c fusion algorithm respectively, and a candidate fused image Imgb obtained by fusion processing using b fusion algorithm and a candidate fused image Imgc obtained by fusion processing using c fusion algorithm can be obtained.

[0078] Step 130, screening multiple candidate fused images based on image quality parameters corresponding to each candidate fused image to obtain a target fused image corresponding to the object to be measured;

[0079] In this step, the image quality parameters may include clarity, saturation, exposure, contrast, etc. of the candidate fused image.

[0080] By screening and processing multiple candidate fusion images based on the image quality parameters corresponding to each candidate fusion image, a candidate fusion image with better image quality can be obtained and determined as a target fusion image.

[0081] Step 140: Adjust the imaging system based on the imaging system parameters and the fusion algorithm corresponding to the target fused image.

[0082] In this step, the target fused image is obtained by fusing a plurality of first images corresponding to the target imaging system parameters in the plurality of imaging system parameters using a target fusion algorithm.

[0083] The target fusion image is an image with a better de-overexposure effect. The imaging system can be adjusted based on the imaging system parameters (ie, target imaging system parameters) and the fusion algorithm (ie, target fusion algorithm) corresponding to the target fusion image.

[0084] For example, the imaging system may be adjusted according to imaging system parameters corresponding to the target fused image.

[0085] The imaging system can perform image fusion based on the fusion algorithm corresponding to the target fusion image to obtain an overexposure suppressed image with better de-exposure effect in industrial inspection.

[0086] According to the imaging system parameter determination method provided in the embodiment of the present application, a plurality of imaging system parameter combinations are used to acquire images of the object to be measured, and at least one fusion algorithm is used to fuse multiple first images under the same set of imaging system parameters, so that multiple candidate fused images can be obtained, so as to automatically infer a target fused image with better quality based on the image quality of each candidate fused image, so that deployment personnel can deploy the imaging system based on the imaging system parameters and the fusion algorithm of the target fused image, so as to obtain high-quality images based on the imaging system, without consuming a lot of manpower and time costs to manually adjust the parameters of the imaging system and the fusion method, thereby improving industrial inspection efficiency.

[0087] In some embodiments, step 130 may include:

[0088] Based on at least one of the clarity, exposure, image quality and saturation corresponding to each candidate fused image, obtaining a target score corresponding to each candidate fused image;

[0089] Based on the target scores corresponding to the candidate fused images, at least one candidate fused image having a target score greater than a target threshold among the multiple candidate fused images is determined as a target fused image.

[0090] In this embodiment, the target score can be used to characterize the de-exposure effect and the fusion effect of the candidate fused image.

[0091] The clarity corresponding to the candidate fused image is used to characterize the visibility of the details of the candidate fused image.

[0092] Exposure is used to characterize information such as brightness of the candidate fused image.

[0093] Image quality is used to characterize image contrast, resolution, texture information, and noise.

[0094] In some embodiments, weights can be assigned to the clarity, exposure, image quality, and saturation of the candidate fused image (for example, the weights can be set to 30% for clarity, 20% for exposure, 40% for image quality, and 10% for saturation), and then the comprehensive score of the candidate fused image, i.e., the target score, is calculated based on the weights.

[0095] The value of the target threshold can be set based on experience, test data or user needs, and this application does not limit it.

[0096] All candidate fusion images may be traversed, their target scores may be compared with the target threshold, and at least one candidate fusion image having a target score greater than the target threshold may be determined as the target fusion image.

[0097] In some embodiments, multiple candidate fusion images can be screened based on at least one of the clarity, exposure, image quality and saturation corresponding to each candidate fusion image, and then the multiple screened candidate fusion images are screened again based on at least one of the clarity, exposure, image quality and saturation until a target number of target fusion images are screened out.

[0098] In some embodiments, obtaining a target score corresponding to each candidate fused image based on at least one of clarity, exposure, image quality, and saturation corresponding to each candidate fused image may include:

[0099] Based on the clarity corresponding to each candidate fused image, a clarity score corresponding to each candidate fused image is calculated, and a plurality of candidate fused images whose clarity scores are greater than a first threshold among the plurality of candidate fused images are determined as a first image set;

[0100] Based on the exposure corresponding to each candidate fused image in the first image set, an exposure score corresponding to each candidate fused image is calculated, and a plurality of candidate fused images in the first image set whose exposure scores are greater than a second threshold are determined as the second image set;

[0101] Based on the image quality and saturation corresponding to each candidate fused image in the second image set, the target score corresponding to each candidate fused image is calculated.

[0102] In this embodiment, an image processing algorithm (such as an edge detection algorithm or a gradient calculation, etc.) may be used to evaluate the clarity of each candidate fused image, and the clarity evaluation result may be converted into a score, and the clarity score may be standardized to a range of 0-100.

[0103] The size of the first threshold can be set based on user needs. For example, the first threshold can be set to 60 or 70, or can be set to other values, which is not limited in this application.

[0104] Images having a clarity score greater than a first threshold may be selected from a plurality of candidate fused images to form a first image set.

[0105] By analyzing the brightness distribution of each candidate fused image in the first image set, the exposure of the candidate fused image can be evaluated, and the exposure evaluation result can be converted into a score, or the exposure score can be standardized to a range of 0-100.

[0106] Images having exposure scores greater than a second threshold may be selected from the first image set to form a second image set.

[0107] For each candidate fused image in the second image set, its image quality and saturation may be evaluated respectively.

[0108] Among them, the candidate fusion image can be evaluated by indicators such as contrast, resolution and noise level.

[0109] The evaluation results of image quality and saturation can be combined to form a comprehensive score (ie, target score).

[0110] According to the target score, several images with the highest scores can be selected from the second fused image set as target fused images.

[0111] For example, the k images with the highest scores can be selected.

[0112] In some embodiments, calculating the target score corresponding to each candidate fused image based on the image quality and saturation corresponding to each candidate fused image in the second image set may include:

[0113] Based on the image quality corresponding to each candidate fused image in the second image set, an image quality score corresponding to each candidate fused image is calculated, and a plurality of candidate fused images in the second image set whose image quality scores are greater than a third threshold are determined as a third image set;

[0114] sorting the plurality of candidate fusion images in the third image set in a target order based on the image quality scores, and assigning a new score to each sorted candidate fusion image to obtain a first standardized score corresponding to each sorted candidate fusion image;

[0115] Based on the saturation corresponding to each candidate fused image in the third image set, a saturation score corresponding to each candidate fused image is calculated;

[0116] sorting the plurality of candidate fusion images in the third image set in target order based on the saturation score, and assigning a new score to each sorted candidate fusion image to obtain a second standardized score corresponding to each sorted candidate fusion image;

[0117] The sum of the first standardized score and the second standardized score corresponding to each candidate fused image in the third image set is determined as the target score corresponding to the candidate fused image.

[0118] In this embodiment, image quality assessment may be performed on each candidate fused image in the second image set. For example, an image quality assessment algorithm (such as PSNR or SSIM) may be used to calculate an image quality score for each image.

[0119] Based on the image quality scores corresponding to the candidate fused images, multiple images with higher scores can be screened out from the second image set as the third image set. The number of images screened out can be determined according to actual needs.

[0120] The candidate fused images in the screened third image set can be sorted from high to low (or from low to high) according to their image quality scores, and then a new score (first standardized score) is assigned to each candidate fused image based on the sorting result. The new score can be a weighted value based on the sorting position to ensure that images that are ranked higher (or lower) have higher scores.

[0121] The saturation score of each candidate fused image in the third image set may be calculated by analyzing the color information in the image. For example, the average saturation of the image may be calculated or other saturation evaluation indicators may be used.

[0122] The candidate fused images in the third image set are sorted from high to low (or from low to high) according to their saturation scores, and then a new score (second standardized score) is assigned to each sorted candidate fused image. The second standardized score may be a weighted value based on the sorting position.

[0123] The first standardized score and the second standardized score of each candidate fused image in the third image set are added together to obtain a target score for each candidate fused image. The target score integrates the evaluation results of the image quality and saturation.

[0124] In the actual implementation process, 2K candidate fusion images can be obtained by performing image processing on the object to be measured based on K sets of imaging system parameters and two fusion algorithms. During fusion, the fusion method and imaging system parameters corresponding to each candidate fusion image can be recorded.

[0125] You can manually input a ROI centered on the image center (to ensure that the object to be tested is located in the center of the image), and then calculate the position in each candidate fused image to complete the initial filtering. Assume that 2K>>k (k is the number of target fused images finally obtained)>=5 (the default final image). In general, 2K is greater than 500.

[0126] Blurred and low-definition candidate fusion images can be filtered out based on the clarity.

[0127] The Tenengrad clarity metric algorithm can be used to obtain the clarity score of each candidate fused image.

[0128] The candidate fusion image I can be calculated by convolution (x,y) Gradients in the x and y directions:

[0129] G x (x,y)=G x *I (x,y) , G y (x,y)=G y *I (x,y)

[0130] Among them, G x (x, y) is the gradient of the candidate fusion image in the x direction, G x is the horizontal gradient calculated using the Sobel operator, G y (x, y) is the gradient of the candidate fusion image in the y direction, G y is the vertical gradient calculated using the Sobel operator,

[0131] Based on the gradient of the candidate fused image in the x direction and the gradient in the y direction, the gradient amplitude can be calculated:

[0132]

[0133] Among them, G(x,y) is the gradient amplitude, G x (x, y) is the gradient of the candidate fusion image in the x direction, G y (x,y) is the gradient of the candidate fused image in the y direction.

[0134] Based on the gradient magnitude, the clarity score of the candidate fused image can be calculated:

[0135]

[0136] Among them, G(x,y) is the gradient amplitude, and T is the clarity score of the candidate fused image.

[0137] Based on the clarity score, the candidate fusion images are sorted, and the top N k Images with low scores are filtered out, N k The value of can be based on user-defined, for example, N k =K, K candidate fusion images with higher scores can be obtained to form the first image set.

[0138] Then, the exposure of each candidate fused image in the first image set may be calculated (the exposure is the deviation from the optimal exposure in the Gaussian curve model), and image filtering may be performed on the first image set.

[0139] A Gaussian curve can be used to obtain the exposure of each pixel near the optimal exposure:

[0140]

[0141] The optimal exposure μ may be set to 0.5, the Gaussian standard deviation σ may be set to 0.2, and x is a pixel value.

[0142] The pixel values ​​of each channel of the candidate fused image may be set to IR, IG, and IB.

[0143] The pixel exposure of each channel can be obtained as follows:

[0144]

[0145] Then, the exposure score of the candidate fused image can be calculated:

[0146] I exposedness =IR exposedness *IG exposedness *IB exposedness

[0147] Sort the candidate fusion images in the first image set according to the exposure score, and the top N c Images with low scores are filtered out, N c The value of can be based on user-defined, for example, Can get The candidate fusion images with higher scores are selected to form the second image set.

[0148] The image quality score of each candidate fused image in the second image set may be obtained based on the model calculation.

[0149] Among them, the model can be trained based on the following steps:

[0150] The dataset used to train the model can be constructed based on the real dataset and the simulated dataset:

[0151] Various industrial products can be collected, including metals, mirrors, plastics, membranes, wafers, and wood products.

[0152] The imaging system can automatically adjust various parameters, such as focal length, exposure, light source distance, and camera distance, and then capture images of industrial products based on the imaging system.

[0153] The multiple images collected are cleaned to remove redundant images, and the number of images after cleaning can be greater than 5k.

[0154] A portion of the above dataset can be taken out (for example, 1k images can be taken out of 5k images), and the 1k images can be added to various categories and different degrees of interference, such as noise, ambient light interference and block effects, to expand the original dataset, for example, to 8k images.

[0155] Then, each sample image in the data set can be annotated based on MOS (Mean Opinion Score, a subjective quality evaluation method) to obtain the score label corresponding to each sample image.

[0156] The model may be a no-reference image quality assessment model (eg, LAR-IQA), and the KAN module in LAR-IQA may be replaced with a multi-layer perceptron (MLP).

[0157] Based on the model, the image quality score of each candidate fused image in the second image set can be obtained, and the first k (for example, 10-20) candidate fused images can be retained to obtain a third image set.

[0158] The candidate fused images in the third image set can be sorted in order of image quality scores from low to high, and scores are reallocated to the sorted candidate fused images, and their scores are standardized to a range of (0-10) to obtain a first standardized score for each candidate fused image. For example, the position of the candidate fused image Img in the third image set is the kth i The first standardized score corresponding to the image can be: k i =1,2,...,k. The larger the ranking number, the higher its first normalized score, and the higher the quality of the candidate fused image.

[0159] According to the saturation scores corresponding to the candidate fused images in the third image set, the k candidate fused images may be sorted in order of the saturation scores from low to high, and the scores may be redistributed to obtain second standardized scores corresponding to the candidate fused images.

[0160] For example, the position of the candidate fusion image is the kth i The second standardized score can be: k i =1,2,...,k, the arrangement order of each candidate fused image can be further adjusted based on the second standardized score.

[0161] The target score of the candidate fused image can be determined based on the first standardized score and the second standardized score of the candidate fused image: score =Img scoreNet +Img scoreS .

[0162] According to the imaging system parameter determination method provided in the embodiment of the present application, image screening is performed based on the clarity and exposure of each candidate fused image, so that higher quality images can be screened out, and then the higher quality images are precisely sorted using the image quality score, and the saturation is used to perform sorting correction, so that a target fused image with a higher overexposure suppression effect can be obtained for deployment personnel to adjust the imaging system, so that the imaging system after adjusting the parameters can generate an image with a better de-overexposure effect.

[0163] The following further describes step 120 by taking the minimum fusion algorithm and the weighted fusion algorithm as examples.

[0164] 1. Small Fusion Algorithm

[0165] In some embodiments, step 120 may include:

[0166] Performing channel separation processing on at least two first images under the same set of imaging system parameters to obtain second images corresponding to each channel under each stroboscopic area;

[0167] Based on the pixel values ​​at the same pixel position in the second image corresponding to each stroboscopic area under the same channel, a third image under the same channel is generated;

[0168] The third images under each channel are fused to obtain a candidate fused image corresponding to the object to be measured.

[0169] In this embodiment, the first images are color images, and each first image can be separated into different color channels. For example, the first image can be separated into a red channel (R channel), a green channel (G channel), and a blue channel (B channel).

[0170] For example, a1, a2 and a3 can be used to represent the first image captured by strobing the bright light in each area under the imaging system parameter a. For example, a1 represents the image captured by the bright light in the first area under the imaging system parameter a, and a2 represents the image captured by the bright light in the second area under the imaging system parameter a.

[0171] By performing channel separation on a1, we can obtain the second image Ra1 in the R channel, the second image Ga1 in the G channel, and the second image Ba1 in the B channel of the image captured by the bright light in the next area of ​​the imaging system parameter a.

[0172] After channel separation of a1, a2 and a3, the second images Ra1, Ga1 and Ba1 corresponding to a1, the second images Ra2, Ga2 and Ba2 corresponding to a2, and the second images Ra3, Ga3 and Ba3 corresponding to a3 can be obtained respectively.

[0173] The same channel has corresponding second images for each stroboscopic area, and the overexposed areas of each second image are different.

[0174] Each pixel position represents the same physical point in each second image.

[0175] The second images have the same size, the width of the second image is m, and the height of the second image is n. For example, the position information of the upper left corner of each second image can be represented as (0, 0), and the position information of the lower right corner of each second image can be represented as (m-1, n-1).

[0176] In the actual implementation process, multiple second images under the same channel can be stacked, each second image can be placed in the same coordinate system, and then the third image can be generated based on the pixel value at the same pixel position in each second image.

[0177] Based on the pixel value at the same pixel position in each second image, the pixel average value, maximum value, minimum value or pixel weighted average value at the pixel position can be calculated, and then the third image can be generated based on the pixel value at each pixel position.

[0178] After the third image of each channel is obtained, the third images of multiple channels may be fused to obtain a candidate fused image.

[0179] In some embodiments, generating a third image under the same channel based on the pixel value at the same pixel position in the second image corresponding to each stroboscopic area under the same channel may include:

[0180] The minimum value of the pixel values ​​at the same pixel position is determined as the target pixel value at the pixel position;

[0181] Based on the target pixel value at each pixel position, a third image is obtained.

[0182] In this embodiment, the pixel values ​​of the same pixel position in different second images may be different.

[0183] For each pixel position, the pixel value of the position may be acquired from all the second images. For example, when the number of the second images is N, each pixel position has N corresponding pixel values.

[0184] In all second images, for each pixel position, the minimum pixel value can be selected as the target pixel value of the position, and the above process is repeated for all pixel positions to obtain a third image consisting of the target pixel values ​​of all pixel positions.

[0185] The third image may reflect the minimum brightness information of all the second images under the same channel.

[0186] In some embodiments, determining the minimum value of pixel values ​​at the same pixel position as the target pixel value at the pixel position may include:

[0187] Stacking multiple second images under the same channel to obtain an image matrix corresponding to the same channel;

[0188] The minimum value of the pixel values ​​at the same pixel position in the image matrix is ​​determined as the target pixel value at the pixel position.

[0189] In this embodiment, the second images of the same channel under the same imaging system parameters may be stacked to obtain an image matrix under the same channel.

[0190] The first dimension of the image matrix is ​​the width of the second image, the second dimension of the image matrix is ​​the height of the second image, and the third dimension of the image matrix is ​​the number of multiple second images under the same channel.

[0191] For example, the width of the second image is m, the height of the second image is n, and the number of the second images in the same channel is x, then the first dimension of the image matrix is ​​m, the second dimension is n, and the third dimension is x.

[0192] Based on the multiple second images corresponding to each channel under each imaging system parameter, multiple image matrices corresponding to each imaging system parameter can be obtained.

[0193] Each image matrix may be sorted in the third dimension according to the size of the pixel value, and then a matrix slice with the smallest pixel value is obtained to obtain the third image.

[0194] In the actual implementation process, Figure 3 As shown, for multiple second images corresponding to the imaging system parameter a: Ra1, Ga1, Ba1, Ra2, Ga2, Ba2, Ra3, Ga3 and Ba3, the second images of the same channel can be stacked to obtain three image matrices: MatrixR = [Ra1, Ra2, Ra3], MatrixG = [Ga1, Ga2, Ga3] and MatrixB = [Ba1, Ba2, Ba3]. Assuming that the image width and height are m and n respectively, the size of each image matrix is ​​m×n×3 (that is, the first dimension is m, the second dimension is n, and the third dimension is 3).

[0195] Sort MatrixR, MatrixG and MatrixB by pixel value in the third dimension, such as Figure 3 As shown, all pixels are sorted along the direction of the arrow.

[0196] Continue to refer Figure 3 In the matrix before sorting, the pixel value at position (0, 0, 0) is 5, the pixel value at position (0, 0, 1) is 8, and the pixel value at position (0, 0, 2) is 6; in the matrix after sorting, the pixel value at position (0, 0, 0) is 5, the pixel value at position (0, 0, 1) is 6, and the pixel value at position (0, 0, 2) is 8.

[0197] The slice with the smallest pixel value in the three slice matrices of the third dimension included in the sorted MatrixR, MatrixG and MatrixB can be taken out and recorded as MatR, MatG and MatB (ie, the third image) respectively.

[0198] 2. Weighted Fusion Algorithm

[0199] In some embodiments, step 120 may include:

[0200] Based on the weight coefficients corresponding to the first images under the same set of imaging system parameters, weighted fusion processing is performed on at least two first images under the same set of imaging system parameters to obtain a candidate fused image.

[0201] In this embodiment, the weight coefficient corresponding to each first image can be determined based on user needs.

[0202] Based on the weight system corresponding to each first image, a weighted sum may be performed on at least two first images corresponding to the same imaging system parameter to obtain a candidate fused image.

[0203] In the actual implementation process, for example, different weight coefficients a1, a2, a3 may be allocated to each of the first images Img1, Img2 and Img3, and a1+a2+a3=1.

[0204] The first images can be subjected to weighted fusion processing based on the weight coefficients to obtain candidate fused images:

[0205] IMG=a1*Img1+a2*Img2+a3*Img3.

[0206] The imaging system parameter determination device provided in the present application is described below. The imaging system parameter determination device described below and the imaging system parameter determination method described above can refer to each other.

[0207] The imaging system parameter determination method provided in the embodiment of the present application may be executed by an imaging system parameter determination device. In the embodiment of the present application, the imaging system parameter determination device executing the imaging system parameter determination method is taken as an example to illustrate the imaging system parameter determination device provided in the embodiment of the present application.

[0208] The embodiment of the present application also provides a device for determining parameters of an imaging system.

[0209] like Figure 4 As shown, the imaging system parameter determination device includes: a first processing module 410 , a second processing module 420 , a third processing module 430 and a fourth processing module 440 .

[0210] A first processing module 410 is used to obtain a plurality of first images corresponding to the object to be measured, wherein the plurality of first images include a plurality of first images acquired by the imaging system under a plurality of imaging system parameters, and the same imaging system parameter corresponds to at least two first images under different stroboscopic regions;

[0211] The second processing module 420 is used to use at least one fusion algorithm to perform fusion processing on at least two first images under the same set of imaging system parameters, and obtain candidate fusion images corresponding to each fusion algorithm under each set of imaging system parameters;

[0212] The third processing module 430 is used to screen and process the multiple candidate fused images based on the image quality parameters corresponding to each candidate fused image to obtain a target fused image corresponding to the object to be measured;

[0213] The fourth processing module 440 is used to adjust the imaging system based on the imaging system parameters and the fusion algorithm corresponding to the target fused image.

[0214] According to the imaging system parameter determination device provided in the embodiment of the present application, a plurality of imaging system parameter combinations are used to acquire images of the object to be measured, and at least one fusion algorithm is used to fuse multiple first images under the same set of imaging system parameters, so that multiple candidate fused images can be obtained, so as to automatically infer a target fused image with better quality based on the image quality of each candidate fused image, so that deployment personnel can deploy the imaging system based on the imaging system parameters and the fusion algorithm of the target fused image, so as to obtain high-quality images based on the imaging system, without consuming a lot of manpower and time costs to manually adjust the parameters of the imaging system and the fusion method, thereby improving industrial inspection efficiency.

[0215] In some embodiments, the third processing module 430 may also be used to:

[0216] Based on at least one of the clarity, exposure, image quality and saturation corresponding to each candidate fused image, obtaining a target score corresponding to each candidate fused image;

[0217] Based on the target scores corresponding to the candidate fused images, at least one candidate fused image having a target score greater than a target threshold among the multiple candidate fused images is determined as a target fused image.

[0218] In some embodiments, the third processing module 430 may also be used to:

[0219] Based on the clarity corresponding to each candidate fused image, a clarity score corresponding to each candidate fused image is calculated, and a plurality of candidate fused images whose clarity scores are greater than a first threshold among the plurality of candidate fused images are determined as a first image set;

[0220] Based on the exposure corresponding to each candidate fused image in the first image set, an exposure score corresponding to each candidate fused image is calculated, and a plurality of candidate fused images in the first image set whose exposure scores are greater than a second threshold are determined as the second image set;

[0221] Based on the image quality and saturation corresponding to each candidate fused image in the second image set, the target score corresponding to each candidate fused image is calculated.

[0222] In some embodiments, the third processing module 430 may also be used to:

[0223] Based on the image quality corresponding to each candidate fused image in the second image set, an image quality score corresponding to each candidate fused image is calculated, and a plurality of candidate fused images in the second image set whose image quality scores are greater than a third threshold are determined as a third image set;

[0224] sorting the plurality of candidate fusion images in the third image set in a target order based on the image quality scores, and assigning a new score to each sorted candidate fusion image to obtain a first standardized score corresponding to each sorted candidate fusion image;

[0225] Based on the saturation corresponding to each candidate fused image in the third image set, a saturation score corresponding to each candidate fused image is calculated;

[0226] sorting the plurality of candidate fusion images in the third image set in target order based on the saturation score, and assigning a new score to each sorted candidate fusion image to obtain a second standardized score corresponding to each sorted candidate fusion image;

[0227] The sum of the first standardized score and the second standardized score corresponding to each candidate fused image in the third image set is determined as the target score corresponding to the candidate fused image.

[0228] In some embodiments, the second processing module 420 may also be used to:

[0229] Performing channel separation processing on at least two first images under the same set of imaging system parameters to obtain second images corresponding to each channel under each stroboscopic area;

[0230] Based on the pixel values ​​at the same pixel position in the second image corresponding to each stroboscopic area under the same channel, a third image under the same channel is generated;

[0231] The third images under each channel are fused to obtain a candidate fused image corresponding to the object to be measured.

[0232] In some embodiments, the second processing module 420 may also be used to:

[0233] Based on the weight coefficients corresponding to the first images under the same set of imaging system parameters, weighted fusion processing is performed on at least two first images under the same set of imaging system parameters to obtain a candidate fused image.

[0234] The imaging system parameter determination device in the embodiment of the present application can be an electronic device, or a component in the electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal, or other devices other than a terminal. Exemplarily, the electronic device can be a mobile phone, a tablet computer, a laptop computer, a PDA, a vehicle-mounted electronic device, a mobile Internet device (Mobile Internet Device, MID), an augmented reality (augmented reality, AR) / virtual reality (virtual reality, VR) device, a robot, a wearable device, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a netbook or a personal digital assistant (personal digital assistant, PDA), etc. It can also be a server, a network attached storage (Network Attached Storage, NAS), a personal computer (personal computer, PC), a television (television, TV), a teller machine or a self-service machine, etc., which is not specifically limited in the embodiment of the present application.

[0235] The imaging system parameter determination device in the embodiment of the present application may be a device having an operating system. The operating system may be an Android operating system, an IOS operating system, or other possible operating systems, which are not specifically limited in the embodiment of the present application.

[0236] The imaging system parameter determination device provided in the embodiment of the present application can achieve Figures 1 to 3 To avoid repetition, the various processes implemented by the method embodiment are not described here.

[0237] In some embodiments, Figure 5 As shown, the embodiment of the present application also provides an electronic device 500, including a processor 501, a memory 502, and a computer program stored in the memory 502 and executable on the processor 501. When the program is executed by the processor 501, each process of the above-mentioned imaging system parameter determination method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0238] It should be noted that the electronic devices in the embodiments of the present application include the mobile electronic devices and non-mobile electronic devices mentioned above.

[0239] On the other hand, the present application also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the various processes of the above-mentioned imaging system parameter determination method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0240] On the other hand, the present application also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the various processes of the above-mentioned imaging system parameter determination method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0241] On the other hand, an embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned imaging system parameter determination method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0242] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0243] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0244] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, by hardware. Based on this understanding, the above technical solution can be essentially or in other words, the part that contributes to the relevant technology can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiment.

[0245] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for determining imaging system parameters, characterized in that: include: Acquire a plurality of first images corresponding to the object to be measured, wherein the plurality of first images include a plurality of first images acquired by the imaging system under a plurality of sets of imaging system parameters, and the same set of imaging system parameters corresponds to at least two first images under different stroboscopic regions; Using at least one fusion algorithm to perform fusion processing on at least two first images under the same group of imaging system parameters, respectively, to obtain candidate fusion images corresponding to each fusion algorithm under each group of imaging system parameters; Screening and processing the plurality of candidate fused images based on the image quality parameters corresponding to the candidate fused images to obtain a target fused image corresponding to the object to be measured; The imaging system is adjusted based on imaging system parameters and a fusion algorithm corresponding to the target fused image.

2. The imaging system parameter determination method according to claim 1, characterized in that: The screening and processing of the plurality of candidate fused images based on the image quality parameters corresponding to the candidate fused images to obtain the target fused image corresponding to the object to be measured includes: Based on at least one of the clarity, exposure, image quality and saturation corresponding to each of the candidate fused images, obtaining a target score corresponding to each of the candidate fused images; Based on the target scores corresponding to the candidate fused images, at least one candidate fused image among the plurality of candidate fused images whose target scores are greater than a target threshold is determined as the target fused image.

3. The imaging system parameter determination method according to claim 2, characterized in that: The obtaining, based on at least one of the clarity, exposure, image quality and saturation corresponding to each of the candidate fused images, a target score corresponding to each of the candidate fused images comprises: Based on the clarity corresponding to each of the candidate fused images, a clarity score corresponding to each of the candidate fused images is calculated, and a plurality of candidate fused images whose clarity scores are greater than a first threshold among the plurality of candidate fused images are determined as a first image set; Based on the exposure corresponding to each candidate fused image in the first image set, calculate the exposure score corresponding to each candidate fused image, and determine a plurality of candidate fused images in the first image set whose exposure score is greater than a second threshold as the second image set; Based on the image quality and saturation corresponding to each of the candidate fused images in the second image set, the target score corresponding to each of the candidate fused images is calculated.

4. The imaging system parameter determination method according to claim 3, characterized in that: The calculating, based on the image quality and saturation corresponding to each of the candidate fused images in the second image set, a target score corresponding to each of the candidate fused images, comprises: Based on the image quality corresponding to each of the candidate fused images in the second image set, an image quality score corresponding to each of the candidate fused images is calculated, and a plurality of candidate fused images in the second image set whose image quality scores are greater than a third threshold are determined as a third image set; sorting the plurality of candidate fused images in the third image set in a target order based on the image quality scores, and assigning a new score to each sorted candidate fused image to obtain a first standardized score corresponding to each sorted candidate fused image; Based on the saturation corresponding to each of the candidate fused images in the third image set, calculating a saturation score corresponding to each of the candidate fused images; sorting the plurality of candidate fused images in the third image set in the target order based on the saturation score, and assigning a new score to each sorted candidate fused image to obtain a second standardized score corresponding to each sorted candidate fused image; The sum of the first standardized score and the second standardized score corresponding to each candidate fused image in the third image set is determined as the target score corresponding to the candidate fused image.

5. The imaging system parameter determination method according to any one of claims 1 to 4, characterized in that: The step of using at least one fusion algorithm to perform fusion processing on at least two first images under the same group of imaging system parameters to obtain candidate fusion images corresponding to each fusion algorithm under each group of imaging system parameters includes: Performing channel separation processing on at least two first images under the same set of imaging system parameters to obtain second images corresponding to each channel under each stroboscopic area; Based on the pixel value at the same pixel position in the second image corresponding to each stroboscopic area under the same channel, a third image under the same channel is generated; The third images under each of the channels are fused to obtain a candidate fused image corresponding to the object to be measured.

6. The imaging system parameter determination method according to any one of claims 1 to 4, characterized in that: The step of using at least one fusion algorithm to perform fusion processing on at least two first images under the same group of imaging system parameters to obtain candidate fusion images corresponding to each fusion algorithm under each group of imaging system parameters includes: Based on the weight coefficients corresponding to the first images under the same set of imaging system parameters, weighted fusion processing is performed on the at least two first images under the same set of imaging system parameters to obtain the candidate fused image.

7. An imaging system parameter determination device, characterized in that: include: A first processing module is used to obtain a plurality of first images corresponding to the object to be measured, wherein the plurality of first images include a plurality of first images acquired by the imaging system under a plurality of sets of imaging system parameters, and the same set of imaging system parameters corresponds to at least two first images under different stroboscopic areas; A second processing module, configured to use at least one fusion algorithm to perform fusion processing on at least two first images under the same group of imaging system parameters, to obtain candidate fusion images corresponding to each fusion algorithm under each group of imaging system parameters; A third processing module, configured to screen and process the plurality of candidate fused images based on the image quality parameters corresponding to the candidate fused images, to obtain a target fused image corresponding to the object to be measured; The fourth processing module is used to adjust the imaging system based on the imaging system parameters and fusion algorithm corresponding to the target fused image.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the imaging system parameter determination method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for determining imaging system parameters as described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the imaging system parameter determination method according to any one of claims 1 to 6 is implemented.

Images