Galvanometer jitter image evaluation method and related device

Abstract

The present application discloses a galvanometer jitter image evaluation method and a related device. The method comprises: obtaining a fused image, wherein the fused image is obtained by fusing images in a real-time image set, and the real-time image set is an image set obtained by a confocal imaging system in left and right preset travel intervals of an optimal focusing position; performing an optical fiber positioning operation in the fused image, to obtain an optical fiber coordinate parameter and a corresponding reference brightness value; on the basis of the optical fiber coordinate parameter and the corresponding reference brightness value, performing a matching operation and a brightness deviation calculation operation on each optical fiber on real-time images in the image set to be processed, to obtain a brightness deviation set, wherein the images in the image set to be processed consist of real-time images having the definition meeting a preset definition requirement in the real-time image set; and performing deviation statistics on the basis of the brightness deviation set, to obtain optical fiber bundle proportion information having the deviation exceeding a preset deviation; taking the optical fiber bundle proportion information as an image jitter value; and performing a galvanometer image jitter processing operation on the basis of the image jitter value and a preset jitter threshold.

Description

A galvanometer dithered image evaluation method and related device

[0001] The present application claims priority to the Chinese patent application No. 202410426951.9 filed on April 10, 2024, entitled "A galvanometer dithered image evaluation method and related device", the entire contents of which are incorporated herein by reference. The disclosure of all related applications that are cited herein and that are in the Applicant's possession are considered part of the disclosure of this application, and are hereby incorporated by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to the field of medical image processing, more specifically, the present application relates to a galvanometer dithered image evaluation method and related device. BACKGROUND

[0003] Confocal laser endomicroscopy is a technology that combines confocal microscopy with traditional endoscopy. This technology can provide real-time subcellular resolution structural information in vivo, and can present cell morphology highly consistent with biopsy pathology imaging, so that doctors can accurately determine cancer, precancerous lesions or health status in situ under the premise of causing minimal discomfort to patients.

[0004] The scanning galvanometer is an important component structure in the confocal imaging system, and its quality directly determines the final imaging quality. The most common abnormality is large amplitude dithering. This kind of abnormality is difficult to detect directly by artificial, therefore, it is necessary to propose a galvanometer dithered image evaluation method, which evaluates whether the galvanometer dithers by calculating the dithering value of the confocal endomicroscopy image, so as to process the dithered image. SUMMARY

[0005] A series of simplified concepts are introduced in the summary section, which will be further described in detail in the detailed description section. The summary section of the present application does not mean to attempt to limit the key features and essential technical features of the claimed technical solutions, nor to determine the protection scope of the claimed technical solutions.

[0006] In a first aspect, the present application proposes a galvanometer dithered image evaluation method, which comprises:

[0007] Obtaining a fusion image, wherein the fusion image is obtained by fusing images in a real-time image set, and the real-time image set is an image set obtained by the confocal imaging system within a preset stroke interval around the best focus position;

[0008] Performing a fiber positioning operation in the fusion image to obtain a fiber coordinate parameter and a corresponding reference brightness value;

[0009] According to the optical fiber coordinate parameters and the corresponding reference brightness value, a matching operation and a brightness deviation calculation operation are performed on each optical fiber on a real-time image in a set of to-be-processed images, to obtain a set of brightness deviations, wherein the images in the set of to-be-processed images are composed of real-time images in the set of real-time images that meet a preset definition requirement;

[0010] Based on the set of brightness deviations, deviation statistics are performed to obtain fiber bundle proportion information of a deviation exceeding a preset deviation;

[0011] The fiber bundle proportion information is taken as an image jitter value;

[0012] Based on the image jitter value and a preset jitter threshold, a galvanometer image jitter processing operation is performed.

[0013] In a feasible implementation, the method further includes:

[0014] The confocal imaging system is controlled to continuously acquire real-time images in a preset stroke interval around the optimal focusing position at a preset step size, to construct N sets of real-time image sets;

[0015] Mean fusion operations are performed on each set of real-time images to obtain N fused images;

[0016] Mask segmentation operations are performed on the N fused images to obtain a set of mask segmentation images;

[0017] Filtering operations are performed on all the mask segmentation images in the set of mask segmentation images using a preset filter kernel to obtain a set of filtered images;

[0018] The filtered images in the set of filtered images are multiplied by a weight mask to obtain a set of weight images;

[0019] Center region pixel value information in all the weight images in the set of weight images is counted, wherein the center region is a region corresponding to the end face of the optical fiber;

[0020] A set of to-be-processed images is determined according to all the center region pixel value information.

[0021] In a feasible implementation, the set of to-be-processed images is determined according to all the center region pixel value information, including:

[0022] The maximum pixel value of all the center region pixel information is counted;

[0023] A definition filtering pixel value is determined according to the maximum pixel value and a preset definition requirement coefficient;

[0024] Real-time images with center region pixel value information greater than the definition filtering pixel value are incorporated into the set of to-be-processed images.

[0025] In an implementation, the fiber positioning operation in the fusion image is performed to obtain the fiber coordinate parameter and the corresponding reference brightness value, including:

[0026] The fiber positioning operation in the fusion image is performed using a local maximum value algorithm to obtain the fiber coordinate parameter and the corresponding reference brightness value.

[0027] In an implementation, the fiber positioning operation in the fusion image is performed using a local maximum value algorithm to obtain the fiber coordinate parameter and the corresponding reference brightness value, including:

[0028] A plurality of anchor frame data is determined in the fusion image using a preset anchor frame and a preset step length;

[0029] The brightness maximum value in all anchor frames and the associated neighborhood and the corresponding coordinate parameter;

[0030] The coordinate parameter is taken as the fiber coordinate parameter;

[0031] The brightness maximum value is taken as the corresponding reference brightness value.

[0032] In an implementation, the matching operation and the brightness deviation calculation operation are performed on each fiber on the real-time image in the set of to-be-processed images according to the fiber coordinate parameter and the corresponding reference brightness value to obtain the brightness deviation set, including:

[0033] In each to-be-processed image, the positioning operation is performed on each fiber based on the fiber coordinate parameter, and the maximum value of the brightness deviation of each fiber in all to-be-processed images is obtained;

[0034] The brightness deviation set is constructed according to the maximum value of the brightness deviation of all fibers.

[0035] In an implementation, the galvanometer image jitter processing operation is performed based on the image jitter value and a preset jitter threshold, including:

[0036] The image removal processing is performed on the jitter value greater than the preset jitter threshold; and / or,

[0037] The image storage processing is performed on the jitter value less than or equal to the preset jitter threshold.

[0038] In a second aspect, the application further provides a galvanometer jitter image evaluation device, including:

[0039] The first acquisition unit is configured to acquire a fusion image, wherein the fusion image is obtained by fusing images in a real-time image set, and the real-time image set is obtained by the confocal imaging system within a preset stroke interval around the best focus position.

[0040] The second acquisition unit is configured to perform a fiber positioning operation on the fusion image to obtain fiber coordinate parameters and corresponding reference brightness values.

[0041] The third acquisition unit is configured to perform a matching operation and a brightness deviation calculation operation on each fiber on a real-time image in a to-be-processed image set according to the fiber coordinate parameters and the corresponding reference brightness values, to obtain a brightness deviation set, wherein the images in the to-be-processed image set are composed of real-time images in the real-time image set that meet a preset definition requirement.

[0042] The fourth acquisition unit is configured to perform deviation statistics based on the brightness deviation set to obtain fiber bundle proportion information whose deviation exceeds a preset deviation.

[0043] The determination unit is configured to take the fiber bundle proportion information as an image jitter value.

[0044] The processing unit is configured to perform a galvanometer image jitter processing operation based on the image jitter value and a preset jitter threshold.

[0045] In a third aspect, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor is configured to implement the steps of the galvanometer jitter image evaluation method of any one of the first aspect when executing the computer program stored in the memory.

[0046] In a fourth aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, and the computer program is executable by a processor to implement the galvanometer jitter image evaluation method of any one of the first aspect.

[0047] In summary, the galvanometer jitter image evaluation method of the embodiment of the present application comprises: obtaining a fusion image, wherein the fusion image is obtained by fusing images in a real-time image set, and the real-time image set is an image set obtained by the confocal imaging system within a preset stroke interval around the best focusing position; performing a fiber positioning operation in the fusion image to obtain fiber coordinate parameters and their corresponding reference brightness values; performing matching operation and brightness deviation calculation operation on each fiber on the real-time image in the to-be-processed image set according to the fiber coordinate parameters and their corresponding reference brightness values, to obtain a brightness deviation set, wherein the images in the to-be-processed image set are composed of real-time images in the real-time image set whose definition meets a preset definition requirement; performing deviation statistics based on the brightness deviation set to obtain fiber bundle proportion information whose deviation exceeds a preset deviation; taking the fiber bundle proportion information as an image jitter value; and performing galvanometer image jitter processing operation based on the image jitter value and a preset jitter threshold. The galvanometer jitter image evaluation method proposed in the embodiment of the present application can efficiently and accurately evaluate the image quality, especially automatically detect the galvanometer jitter problem, greatly improving the accuracy and efficiency of diagnosis and analysis. Through the realization of automatic definition calculation and jitter value evaluation, low-quality images can be immediately identified and removed, while images meeting the quality requirements are retained, ensuring the quality and integrity of the data set, which is helpful for real-time feedback and decision-making medical diagnosis. The disclosure significantly improves the imaging quality and application efficiency of the confocal laser endoscope by introducing automatic image processing technology, providing a powerful tool for doctors.

[0048] The galvanometer jitter image evaluation method proposed in the present application, other advantages, objects and features of the present application will be embodied in part through the following description, and will be understood by those skilled in the art through research and practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0049] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are for purposes of illustration only and are not considered a limitation of the present specification. Moreover, like reference numerals are used to designate like parts throughout the specification and drawings. In the drawings:

[0050] FIG. 1 is a flowchart of a galvanometer jitter image evaluation method provided by an embodiment of the present application;

[0051] FIG. 2 is a schematic diagram of an image before mask segmentation provided by an embodiment of the present application;

[0052] FIG. 3 is a schematic diagram of a mask plate according to an embodiment of the present application;

[0053] FIG. 4 is a schematic diagram of a mask plate after segmentation according to an embodiment of the present application;

[0054] FIG. 5 is a schematic diagram of a preset filter kernel according to an embodiment of the present application;

[0055] FIG. 6 is a partial view of an end face of an optical fiber according to an embodiment of the present application;

[0056] FIG. 7 is a partial view of positioning an optical fiber at an end face of the optical fiber according to an embodiment of the present application;

[0057] FIG. 8 is a schematic diagram of overall positioning of an end face of an optical fiber according to an embodiment of the present application;

[0058] FIG. 9 is a schematic diagram of a principle of determining a brightness value according to an embodiment of the present application;

[0059] FIG. 10 is a schematic diagram of a structure of a galvanometer dithered image evaluation device according to an embodiment of the present application;

[0060] FIG. 11 is a schematic diagram of a structure of a galvanometer dithered image evaluation electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0061] The terms "first", "second", "third", "fourth" and the like in the description and the claims of the present application and the above-described drawings (if any) are used to distinguish similar objects, and do not necessarily have to be described in a specific order or chronological order. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device. The technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all embodiments.

[0062] Referring to FIG. 1, a flowchart of a galvanometer dithered image evaluation method according to an embodiment of the present application is shown, which can specifically include:

[0063] S110, obtaining a fusion image, wherein the fusion image is obtained by fusing images in a real-time image set, and the real-time image set is an image set obtained by a confocal imaging system within a preset stroke interval around the best focus position;

[0064] For example, the confocal imaging system acquires a series of real-time images within a preset travel interval around the best focus position. These images are fused into a single image to improve the clarity and detail of the image. This fusion technique can utilize the best information from multiple images to generate an image with higher quality.

[0065] S120, fiber positioning operation is performed in the fused image to obtain fiber coordinate parameters and their corresponding reference brightness values;

[0066] For example, in the fused image, the fiber positioning operation is performed to determine the coordinate parameters and reference brightness values of the optical fiber. The precise position of the optical fiber in the image is identified, and its brightness is measured. These data provide important basic information for subsequent image processing.

[0067] S130, according to the fiber coordinate parameters and their corresponding reference brightness values, the matching operation and brightness deviation calculation operation are performed on each optical fiber in the real-time images in the set of images to be processed to obtain a set of brightness deviations, wherein the images in the set of images to be processed are composed of real-time images whose clarity meets the preset clarity requirement;

[0068] For example, the fiber coordinate parameters and reference brightness values obtained from the fused image are used to perform matching operation on each optical fiber in the real-time images in the set of images to be processed. The purpose of the matching operation is to identify the corresponding optical fiber in the image to be processed and calculate its brightness deviation. The set of images to be processed is composed of real-time images whose clarity meets the preset requirement, and the images that do not meet the clarity requirement are removed, which can accurately track the position and brightness change of the optical fiber in different images.

[0069] S140, based on the set of brightness deviations, deviation statistics are performed to obtain the proportion information of the fiber bundle whose deviation exceeds the preset deviation;

[0070] For example, based on the set of brightness deviations obtained in the previous step, statistical analysis is performed to obtain the proportion information of the fiber bundle whose deviation exceeds the preset standard. The central region of the entire image, i.e. the fiber region, is analyzed, and the proportion of the fiber brightness deviation exceeding the normal range is counted to evaluate the overall quality of the image or the problem of a specific region.

[0071] S150, the proportion information of the fiber bundle is taken as the image jitter value;

[0072] For example, the proportion information of the fiber bundle obtained in the previous step is taken as the basis for the image jitter value, and the image stability is evaluated through the jitter value.

[0073] S160, based on the image jitter value and the preset jitter threshold, a galvanometer image jitter processing operation is performed.

[0074] Finally, based on the image jitter value and the preset jitter threshold, the image is subjected to a galvanometer image jitter processing operation.

[0075] Specifically, the method can be completed by a data acquisition module, a sharpness calculation module, and a jitter value calculation module. The data acquisition module is responsible for acquiring real-time images of the confocal imaging system at different focusing positions. After the images are acquired, they are input into the sharpness calculation module. The fusion image is calculated from the real-time images, and the image sharpness is determined from the fusion image. The real-time images with sharpness meeting the requirements are selected as the images to be processed. Finally, the selected images to be processed are input into the jitter value calculation module. The jitter calculation module calculates the image jitter value using the method of S110-S160, compares the jitter value with the preset jitter threshold, and determines that the jitter condition is unacceptable if the jitter value is greater than the preset jitter threshold, and the jitter condition is acceptable if the jitter value is less than the preset jitter threshold.

[0076] In summary, the galvanometer jitter image evaluation method proposed in the embodiments of the present application can efficiently and accurately evaluate image quality, especially automatically detect galvanometer jitter problems, greatly improving the accuracy and efficiency of diagnosis and analysis. By implementing automatic sharpness calculation and jitter value evaluation, low-quality images can be immediately identified and removed, while images meeting the quality requirements are retained, ensuring the quality and integrity of the data set, which is helpful for real-time feedback and decision-making in medical diagnosis. The present disclosure significantly improves the imaging quality and application efficiency of the confocal laser endoscope by introducing automatic image processing technology, providing a powerful tool for doctors.

[0077] In a feasible implementation, the method further includes:

[0078] The confocal imaging system is controlled to continuously acquire real-time images in a preset stroke interval around the optimal focusing position with a preset step size to construct N sets of real-time image sets.

[0079] Mean fusion operations are performed on each of the real-time image sets to obtain N fusion images.

[0080] Mask segmentation operations are performed on the N fusion images to obtain a mask segmentation image set.

[0081] Filtering operations are performed on all mask segmentation image sets in the mask segmentation image set using a preset filter kernel to obtain a filtered image set.

[0082] The filtered images in the filtered image set are multiplied by a weight mask to obtain a weight image set;

[0083] The center region pixel value information in all the weight images in the weight image set is counted, wherein the center region is a region corresponding to the fiber end face;

[0084] The image set to be processed is determined according to all the center region pixel value information.

[0085] For example, the confocal imaging system is controlled to continuously acquire real-time images in a preset stroke interval on the left and right of the best focus position with a preset step size. Thus, a sufficient number and quality of images are obtained to construct N groups of real-time image sets, which will be used for subsequent processing and analysis. Mean fusion operation is performed on all images in each acquired real-time image set to generate N fused images. Mean fusion improves the signal-to-noise ratio and clarity of the images by calculating the average of the pixel values of all images in each real-time image set. Mask segmentation operation is performed on the N fused images to obtain a mask segmentation image set. A mask is defined to distinguish the region of interest and the background in the image. The mask segmented image set is subjected to filtering operation using a preset filter kernel. The purpose of filtering is to remove noise in the image or emphasize certain features to improve image quality. The filtered images are multiplied by a weight mask to obtain a weight image set. The weight mask is used to adjust the importance of each pixel value in the image, which can emphasize or weaken the influence of certain regions to better analyze the image. The pixel value information of the center region (i.e., the region corresponding to the fiber end face) of all images in the weight image set is counted. Based on the pixel value information of the center region, the image set to be processed is determined.

[0086] Specifically, the data acquisition module can be responsible for acquiring data at different focus positions. First, the best focus position P of the confocal imaging system is found. Next, the motor is moved at a fixed step size S in the search range (P-H, P+H) with H as the search half-field. A group of images is acquired at each focus position, and the frame number is F. N groups of image data are obtained.

[0087] For the sequence images collected by the data acquisition module at different focusing positions, a single set of fused images is selected to calculate the sharpness thereof as the sharpness value C of the set of images. The fusion image of the set of images can be obtained in a mean fusion manner, i.e., the average value of the corresponding pixel points in the sequence images (real-time images) is taken as the pixel value of the corresponding point of the fusion image. After obtaining the fusion image of the set of images, mask segmentation calculation is performed, and the purpose of the mask segmentation calculation is to remove the interference outside the fiber end face. The images before and after mask segmentation are shown in FIGS. 2-4, respectively. Next, the set of mask segmentation images after the mask operation is filtered using a preset filter kernel, as shown in FIG. 5. Then, the set of filtered images is multiplied by a weight mask, and the size of the weight mask can be 0.1-1, and the size decreases from the center to the periphery. Finally, the size of the pixel value in the central region is counted as the image sharpness. According to all the pixel value information in the central region, the sharpness is screened to determine the set of images to be processed.

[0088] The method provided in the embodiment accurately screens images meeting the preset sharpness requirement, thereby providing a solid foundation for high-quality image analysis, and significantly improving efficiency, ensuring quality, and improving accuracy.

[0089] In a possible implementation, the determining of the set of images to be processed according to all the pixel value information in the central region includes:

[0090] Counting the maximum pixel value of all the pixel information in the central region;

[0091] Determining a sharpness screening pixel value according to the maximum pixel value and a preset sharpness requirement coefficient;

[0092] Incorporating the real-time image with the pixel value information greater than the sharpness screening pixel value into the set of images to be processed.

[0093] For example, the maximum pixel value is counted from the pixel value information in the central region (i.e., the region corresponding to the fiber end face) of all images. This maximum value represents the highest point of the brightness of the fiber end face in the entire set of images, and is a key indicator indicating the pixel intensity that can be reached by the brightest region.

[0094] A sharpness screening pixel value is determined according to the maximum pixel value and a preset sharpness requirement coefficient. The sharpness requirement coefficient is a predefined threshold value for adjusting the standard for selecting images to ensure that the selected images have sufficient sharpness. In this way, it can be ensured that the images in the set of images to be processed meet a certain quality standard and are suitable for further analysis and processing.

[0095] The real-time image with pixel value information in the central region greater than the definition screening pixel value is incorporated into the image set to be processed. From the original or preliminary processed image set, those images with brightness (i.e. pixel value) higher than a certain threshold value are screened. These images are considered to have sufficient definition and can represent the clearest and brightest state of the fiber end face, and thus are selected as part of the image set to be processed.

[0096] Specifically, the N groups of image data collected by the data collection module are all input into the definition calculation module to obtain a definition value set containing N different groups of focus position images, and then the maximum value Cmax is obtained. The range of the preset definition requirement coefficient is 0.6-1, and for example, it can be determined as 0.8. Next, the real-time image is traversed, and if it is greater than 0.8Cmax, the element is added to the new set to form the image set to be processed.

[0097] In a feasible implementation, the above fiber positioning operation in the above fusion image is performed to obtain the fiber coordinate parameter and the corresponding reference brightness value, including:

[0098] The local maximum value algorithm is used in the above fusion image to perform the fiber positioning operation to obtain the fiber coordinate parameter and the corresponding reference brightness value.

[0099] For example, as shown in FIGS. 6-8, after positioning the fiber by the local maximum value algorithm, the position of the fiber end face in the image, i.e. the coordinate parameter of the fiber, can be accurately determined. These parameters provide accurate information of the fiber position, so that the fiber can be analyzed and processed in a targeted manner. In addition to positioning the fiber, the local maximum value algorithm can also help determine the reference brightness value of the fiber end face. The reference brightness value refers to the brightness level of the fiber end face in the fusion image, which serves as a reference for comparing brightness deviation in subsequent steps.

[0100] The method proposed in the embodiments of the present application uses the local maximum value algorithm to perform fiber positioning in the fusion image, which not only improves the accuracy of fiber positioning, but also lays a solid foundation for subsequent image processing and analysis work. This method is particularly suitable for fiber imaging applications that require high-precision positioning and brightness analysis, and can effectively improve the imaging quality and the accuracy of analysis.

[0101] In a feasible implementation, the above fiber positioning operation in the above fusion image is performed to obtain the fiber coordinate parameter and the corresponding reference brightness value, including:

[0102] A plurality of anchor frame data are determined in the above fusion image by using a preset anchor frame and a preset step size;

[0103] The brightness maximum value and the corresponding coordinate parameter in all anchor frames and their associated neighborhoods;

[0104] The above coordinate parameter is taken as the above fiber coordinate parameter;

[0105] The above brightness maximum value is taken as the corresponding reference brightness value thereof.

[0106] For example, by arranging a plurality of preset anchor frames in the fusion image and moving the anchor frames by a preset step, the entire image can be efficiently searched to find possible fiber positioning points. In each anchor frame and its neighborhood, a brightness maximum point, i.e., the brightest point in the image, is searched. Whenever a brightness maximum is found, the coordinate parameter of the point is recorded, which represents the specific position of the fiber end face in the image. The coordinate parameter determined by the above method is directly used as the coordinate parameter of the fiber, which provides accurate spatial positioning information for subsequent image processing and analysis. The brightness maximum found in each anchor frame is used as the reference brightness value of the fiber end face. This reference value serves as the basis for calculating the brightness deviation and helps to evaluate the brightness change of the fiber end face.

[0107] Specifically, first, 3x3 anchor frame traversal is performed on the image with a step of 3, the brightness maximum value in the anchor frame and its position coordinate are obtained, then it is determined whether the maximum value is greater than the maximum value in its eight-neighborhood, if yes, the position coordinate of the point is stored and recorded as a fiber coordinate, if no, the anchor frame is moved to find the maximum value again and then determine, until the image traversal is completed, all fiber coordinate parameters and their corresponding brightness values are found, which are taken as the reference fiber coordinate parameters and the corresponding reference brightness values. As shown in FIG. 9, after obtaining the fiber coordinate parameter and the corresponding brightness value of each fiber, for a single fiber, the real-time image is matched for positioning, and the maximum brightness value in the 13-neighborhood (including the point itself) of the coordinate L1 is found as the real-time brightness value of the fiber in the real-time image, wherein the 13-neighborhood is the green and white area in FIG. 9.

[0108] The method proposed in the embodiments of the present application efficiently and accurately locates the fiber in the fusion image by combining the anchor frame, the step and the local maximum value algorithm, and obtains the reference brightness value thereof, which provides a solid foundation for high-quality fiber imaging and subsequent image processing.

[0109] In a feasible implementation, the above matching operation and brightness deviation calculation operation on each fiber in the real-time image in the set of to-be-processed images according to the above fiber coordinate parameter and the corresponding reference brightness value thereof to obtain the brightness deviation set includes:

[0110] In each to-be-processed image, based on the above fiber coordinate parameter, a positioning operation is performed on each fiber, and the maximum value of the brightness deviation of each fiber in all to-be-processed images is obtained;

[0111] The brightness deviation set is constructed according to the maximum values of the brightness deviations of all fibers.

[0112] For example, first, in each image to be processed, each optical fiber is positioned according to the previously obtained fiber coordinate parameters. In each image to be processed, the system will look for the position of the optical fiber that matches the known coordinate parameters. Once each optical fiber is located in the image to be processed, the next step is to calculate the brightness deviation of each optical fiber in all images to be processed. The brightness deviation refers to the difference between the brightness of the optical fiber in the image to be processed and the reference brightness value in the fusion image. Then, for each optical fiber, the maximum value of the brightness deviation in all images to be processed is determined. The maximum value indicates the extreme case of the brightness change of the optical fiber, which is used to evaluate the image quality and the condition of the optical fiber. Based on the above process, the maximum values of the brightness deviation of all optical fibers are collected to form a brightness deviation set. This set contains the maximum deviation of all optical fibers in the image set to be processed relative to the reference brightness value, providing basic data for further analysis.

[0113] Specifically, the brightness deviation can be calculated by the following formula:

[0114] In a possible implementation, the mirror image jitter processing operation based on the image jitter value and the preset jitter threshold value includes:

[0115] The image removal processing is performed on the jitter value greater than the preset jitter threshold value; and / or,

[0116] The image storage processing is performed on the jitter value less than or equal to the preset jitter threshold value.

[0117] For example, when the jitter value of the image is greater than the preset jitter threshold value, it indicates that the jitter degree of the image is too high, which may seriously affect the image quality and the accuracy of the analysis result. Therefore, these images are marked as unqualified and removed, that is, not selected into the final image set or the analysis process.

[0118] When the jitter value of the image is less than or equal to the preset jitter threshold value, it means that the jitter degree of the image is within an acceptable range, and the image quality is considered to be qualified. Therefore, these images are retained for storage processing for subsequent analysis and use, ensuring the integrity and high quality of the data set.

[0119] Referring to FIG. 10, one embodiment of the mirror jitter image evaluation device in the present application can include:

[0120] The first acquisition unit 21 is configured to acquire a fusion image, wherein the fusion image is obtained by fusing images in a real-time image set, and the real-time image set is an image set obtained by the confocal imaging system within a preset stroke interval around the best focus position.

[0121] The second acquisition unit 22 is configured to perform fiber positioning operation on the fusion image to obtain fiber coordinate parameters and corresponding reference brightness values.

[0122] The third acquisition unit 23 is configured to perform matching operation and brightness deviation calculation operation on each fiber in a real-time image in a set of to-be-processed images according to the fiber coordinate parameters and corresponding reference brightness values, to obtain a set of brightness deviations, wherein the images in the set of to-be-processed images are composed of real-time images in the set of real-time images whose clarity meets a preset clarity requirement.

[0123] The fourth acquisition unit 24 is configured to perform deviation statistics based on the set of brightness deviations to obtain fiber bundle proportion information whose deviation exceeds a preset deviation.

[0124] The determination unit 25 is configured to take the fiber bundle proportion information as an image jitter value.

[0125] The processing unit 26 is configured to perform galvanometer image jitter processing operation based on the image jitter value and a preset jitter threshold.

[0126] As shown in FIG. 11, the embodiment of the present application further provides an electronic device 300, which comprises a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and capable of running on the processor, and the processor 320 implements the steps of any method for evaluating a galvanometer jitter image according to the computer program 311.

[0127] Since the electronic device introduced in the embodiment is a device used to implement the galvanometer jitter image evaluation device in the embodiment of the present application, the specific implementation of the electronic device and its various changes can be understood by those skilled in the art based on the method introduced in the embodiment of the present application. Therefore, how the electronic device implements the method in the embodiment of the present application will not be described in detail, and any device used by those skilled in the art to implement the method in the embodiment of the present application is within the scope of the present application.

[0128] In the specific implementation process, the computer program 311 can implement any embodiment in the corresponding embodiment of FIG. 1 when executed by the processor.

[0129] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0130] Those skilled in the art will appreciate that embodiments of the application can be readily used as a method, a system or a computer program product. Accordingly, the application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the application can take the form of a computer program product on one or more computer readable storage media (including, but not limited to, disk memory, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0131] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks or in combination with the flowchart block or blocks.

[0132] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks or combination thereof.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks or in combination with the flowchart block or blocks.

[0134] Embodiments of the present application also provide a computer program product, which comprises computer software instructions, when the computer software instructions are run on a processing device, cause the processing device to perform the galvanometer dithering image evaluation process in the corresponding embodiments

[0135] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that the computer can store or be integrated into a data storage device such as a server, data center, etc. containing one or more available media. The available media can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk (SSD)), etc.

[0136] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0137] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or components shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0138] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0139] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit.

[0140] If the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the present application, essentially or the part that contributes to the prior art, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program codes that can be stored in the medium.

[0141] The above, the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application. Industrial applicability

[0142] The galvanometer dithering image evaluation method provided by the embodiments of the present application can efficiently and accurately evaluate image quality, especially automatically detect galvanometer dithering problems, greatly improving the accuracy and efficiency of diagnosis and analysis, through automatic image fusion, optical fiber positioning, brightness deviation calculation and evaluation based on dithering values. Therefore, the galvanometer dithering image evaluation method and related devices provided by the embodiments of the present application have industrial applicability.

Claims

1. A method for evaluating a galvanometer mirror jitter image, characterized in that: include: Acquire a fused image, wherein the fused image is obtained by fusing images in a real-time image set, wherein the real-time image set is an image set acquired by the confocal imaging system within a preset travel range around an optimal focus position; Performing an optical fiber positioning operation in the fused image to obtain optical fiber coordinate parameters and their corresponding reference brightness values; performing a matching operation and a brightness deviation calculation operation on each optical fiber on a real-time image in a to-be-processed image set according to the optical fiber coordinate parameters and their corresponding reference brightness values ​​to obtain a brightness deviation set, wherein the images in the to-be-processed image set are composed of real-time images in the real-time image set whose clarity meets a preset clarity requirement; Performing deviation statistics based on the brightness deviation set to obtain information on the proportion of optical fiber bundles whose deviations exceed a preset deviation; Using the fiber bundle ratio information as an image jitter value; A galvanometer image jitter processing operation is performed based on the image jitter value and a preset jitter threshold.

2. The galvanometer mirror jitter image evaluation method according to claim 1, characterized in that: Also includes: Controlling the confocal imaging system to continuously acquire real-time images with a preset step length within a preset travel range around the optimal focus position to construct N sets of real-time image sets; Performing a mean fusion operation on each group of real-time images to obtain N fused images; Performing a mask segmentation operation on the N fused images to obtain a mask segmentation image set; performing a filtering operation on all mask segmentation image sets in the mask segmentation image set using a preset filter kernel to obtain a filtered image set; multiplying a filtered image in the filtered image set by a weight mask to obtain a weighted image set; Counting pixel value information of the central area of ​​all weight images in the weight image set, wherein the central area is the area corresponding to the optical fiber end face; A set of images to be processed is determined based on all the central area pixel value information.

3. The galvanometer mirror jitter image evaluation method according to claim 2, characterized in that: Determining the image set to be processed based on all the central area pixel value information includes: Counting the maximum pixel value of all pixel information in the central area; Determining a definition screening pixel value according to the maximum pixel value and a preset definition requirement coefficient; The real-time image whose central area pixel value information is greater than the clarity screening pixel value is incorporated into the set of images to be processed.

4. The galvanometer mirror jitter image evaluation method according to claim 1, characterized in that: The performing of the optical fiber positioning operation in the fused image to obtain optical fiber coordinate parameters and their corresponding reference brightness values ​​includes: A local maximum algorithm is used in the fused image to perform an optical fiber positioning operation to obtain optical fiber coordinate parameters and their corresponding reference brightness values.

5. The galvanometer mirror jitter image evaluation method according to claim 4, characterized in that: The optical fiber positioning operation is performed using a local maximum algorithm in the fused image to obtain optical fiber coordinate parameters and their corresponding reference brightness values, including: Determining a plurality of anchor frame data in the fused image using a preset anchor frame and a preset step size; The brightness maximum value and its corresponding coordinate parameters in all anchor boxes and their associated neighborhoods; Using the coordinate parameters as the optical fiber coordinate parameters; The maximum brightness value is used as its corresponding reference brightness value.

6. The galvanometer mirror jitter image evaluation method according to claim 1, characterized in that: The matching operation and brightness deviation calculation operation are performed on each optical fiber in the real-time image in the image set to be processed according to the optical fiber coordinate parameters and the corresponding reference brightness values ​​to obtain a brightness deviation set, including: performing a positioning operation on each optical fiber in each image to be processed based on the optical fiber coordinate parameters, and obtaining a maximum value of the brightness deviation of each optical fiber in all images to be processed; The brightness deviation set is constructed according to the maximum value of the brightness deviations of all optical fibers.

7. The galvanometer mirror jitter image evaluation method according to claim 1, characterized in that: The performing of a galvanometer image jitter processing operation based on the image jitter value and a preset jitter threshold comprises: Perform image removal processing if the jitter value is greater than the preset jitter threshold; and / or, The image storage processing is performed when the jitter value is less than or equal to the preset jitter threshold.

8. A galvanometer jitter image evaluation device, characterized in that: include: A first acquisition unit is configured to acquire a fused image, wherein the fused image is obtained by fusing images in a real-time image set, wherein the real-time image set is an image set acquired by the confocal imaging system within a preset travel range around an optimal focus position; a second acquisition unit, configured to perform an optical fiber positioning operation on the fused image to obtain optical fiber coordinate parameters and corresponding reference brightness values; a third acquiring unit, configured to perform a matching operation and a brightness deviation calculation operation on each optical fiber in a real-time image in a to-be-processed image set according to the optical fiber coordinate parameters and the corresponding reference brightness values, so as to acquire a brightness deviation set, wherein the images in the to-be-processed image set are composed of real-time images in the real-time image set whose clarity meets a preset clarity requirement; a fourth acquiring unit, configured to perform deviation statistics based on the brightness deviation set to acquire information on a proportion of optical fiber bundles whose deviations exceed a preset deviation; a determining unit, configured to use the optical fiber bundle ratio information as an image jitter value; A processing unit is used to perform a galvanometer image jitter processing operation based on the image jitter value and a preset jitter threshold.

9. An electronic device comprising: A memory and a processor, wherein the processor is configured to implement the steps of the galvanometer mirror jitter image evaluation method according to any one of claims 1 to 7 when executing a computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the galvanometer mirror jitter image evaluation method according to any one of claims 1 to 7 are implemented.

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