Microscopic imaging system for detecting micro lens

Abstract

The invention relates to the technical field of imaging detection, in particular to a microscopic imaging system for detecting a microlens, which comprises a light source path establishing module, an initial image position acquisition module, a peak coordinate recording module, a stability offset analysis module and an imaging quality judgment module. According to the invention, the numerical aperture normalization processing is carried out on the direction of the light passing through the specific aperture area, so that the precise control of the spatial distribution characteristic of the light beam is realized, the spatial mapping of the focus position is completed on the basis of the light in the unified direction, the direction consistency and the imaging stability in the image forming process are improved, and the imaging quality is improved. By combining continuous peak coordinate search of a light spot focusing position, a light spot change trend can be dynamically obtained, imaging deviation caused by infinitesimal displacement can be effectively distinguished, and an accurate marking mechanism for light spot stability is constructed by means of absolute difference extraction of three-dimensional offset data and Rayleigh criterion tolerance judgment. And the response sensitivity and the analysis reliability of weak instability factors in the imaging performance of the micro lens are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of imaging detection technology, and in particular to a microscopic imaging system for detecting microlenses. BACKGROUND

[0002] The field of imaging detection technology relates to technical methods for image acquisition and analysis of microscopic or macroscopic targets using optical imaging devices, including optical microscopic imaging technology, digital image acquisition and restoration technology, optical element performance detection, target size measurement, and surface defect detection, and quantitative and qualitative evaluation of the structure and performance of the measured object is realized through various forms of imaging devices and analysis methods. The traditional microscopic imaging system for detecting microlenses refers to a device for detecting the imaging performance of a microlens array or a single microlens, and the imaging quality of the microlens is detected and analyzed. The measured microlens is positioned and illuminated using a microscope objective, an illumination light source, a CCD (or CMOS, TDI) imaging component, and a translation positioning mechanism on a detection table, and the imaging performance of the microlens is evaluated by acquiring image data formed by the microlens and measuring optical distortion and resolution.

[0003] The existing microscopic imaging technology for detecting microlenses mainly detects the microlens through CCD image acquisition and positioning mechanisms. The core relies on single acquisition and calculation of static images. In the case of slight shift of light spots or continuous instability caused by external interference, it is difficult to realize continuous tracking of the change process and sensitive response to slight dynamic deviation. In particular, in the scenario where light spot jumping phenomenon frequently occurs between units in a microlens array, the traditional image evaluation method is difficult to accurately calibrate the focal point stability, resulting in that part of the slight distortion cannot be identified in the early stage, thereby affecting the overall imaging consistency and quality control of the microstructure, and limiting the applicable range and recognition depth in high-precision optical device detection. SUMMARY

[0004] The purpose of the present application is to solve the problems existing in the prior art and to provide a microscopic imaging system for detecting microlenses.

[0005] In order to achieve the above purpose, the present application adopts the following technical scheme: a microscopic imaging system for detecting microlenses comprises: The light source path establishment module obtains the light beam convergence point formed by the light source at the first condenser end, detects the spatial coordinate distribution of the light rays passing through the first aperture diaphragm at the convergence point, performs coordinate region classification processing, normalizes the NA numerical aperture of the light beam falling into the light transmission region, and obtains the collimated light transmission direction parameter group; The initial image position acquisition module projects and reflects all light rays with consistent directions within the field stop range according to the collimated light transmission direction parameter group, captures the concentrated focus position of the reflected light rays at the second condenser, records the coordinate information at the focus, and generates a focus position space projection group; The peak coordinate recording module selects a light spot calibration point and performs frame-by-frame peak position search on the light spot at the calibration point according to the focus position space projection group, records the peak coordinate information, obtains the absolute value of the coordinate difference of any two frames, and obtains a calibration point peak continuous coordinate sequence. The stability offset analysis module extracts the three-dimensional offset absolute value data between adjacent peak coordinates in all frames based on the calibration point peak continuous coordinate sequence, judges whether it falls within the Rayleigh criterion tolerance range, marks all abnormal frame numbers in the data sequence, and obtains a light spot stability offset annotation sequence.

[0006] As a further scheme of the application, the collimated light transmission direction parameter group includes spatial direction distribution recording, numerical aperture normalization parameter, and direction normalization coordinate system, the focus position space projection group includes light spot geometric center projection coordinate, reflection offset parameter, and focus positioning space data, the calibration point peak continuous coordinate sequence includes light spot peak three-dimensional coordinate change amount, inter-frame displacement amplitude data, and time sequence displacement feature, and the light spot stability offset annotation sequence includes abnormal frame number, three-dimensional offset abnormality identification, and offset abnormality time index.

[0007] As a further scheme of the application, the light source path establishment module includes: The light beam convergence extraction submodule acquires the light beam divergence direction information and the emission intensity change value of the first condenser based on the emission path formed under the continuous emission state of the light source, obtains the angle vector sequence and the unit energy identification sequence of the emission path direction, identifies the positional relationship of each direction vector in space, identifies the focusing trend in combination with the incident angle characteristics, screens the intersection regions meeting the focusing characteristics, and generates a focusing convergence coordinate set; The space traversal coordinate determination submodule traces each intersection point to the first aperture stop position along the incident direction according to all intersection point position information in the focusing convergence coordinate set, identifies the traversal state of the intersection point in combination with the boundary space definition characteristics of the aperture stop, classifies the light shielding and the light transmission according to the relative relationship between the boundary position and the incident direction, extracts the spatial position and direction vector feature of the light transmission type intersection point, and generates a light transmission direction distribution set. The normalized direction calculation submodule judges the incident matching state according to the spatial relationship between the direction features of each direction vector in the light transmission direction distribution set and the optical axis of the collimator, classifies the direction information meeting the spatial pointing requirements into a unified direction standard system, and performs direction vector normalization processing to obtain the collimated light transmission direction parameter group.

[0008] As a further scheme of the present application, the initial position acquisition module comprises: The directional light ray projection submodule extracts the spatial direction characteristic of each directional vector based on all the light transmission direction vectors in the collimated light transmission direction parameter set, identifies whether it is located within the spatial angle range allowed by the field stop, filters out the directional data conflicting with the field stop boundary, retains the directional information that can form a projection path within the effective projection range, projects all the directional consistent light rays within the field stop range, and generates a field direction projection set; The reflection focal point capture submodule identifies the intersection position with the single-surface mirror in space based on the directional path information in the field direction projection set, extracts the spatial angle relationship between the corresponding incident path and the reflection surface, confirms the reflection direction of each path according to the light path extension rule, calibrates the expected position area focused at the second condenser, judges the concentrated area through the intersection trend of the reflection paths, detects the concentrated area, extracts the path convergence distribution of each focal area, and obtains the focal center point formed by the reflection paths, and establishes a reflection focal position set; The light spot coordinate calculation submodule analyzes the geometric boundary form of the corresponding spot area according to the extracted spatial point data in the reflection focal position set, identifies the intensity change center in each spot area, extracts the geometric contour axis of the spot through tracking the change trend of the gray scale edge, identifies the geometric center position of each spot area, integrates all the geometric center position information, and obtains a focal position spatial projection set.

[0009] As a further scheme of the present application, the peak coordinate recording module comprises: The aperture calibration submodule extracts the projection points meeting the requirements of the objective lens back focal plane range based on all the light spot coordinate data recorded in the focal position spatial projection set, judges the matching degree with the laser scanning structure, obtains the relative imaging plane position of the projection position, selects the light spot position corresponding to the first aperture stop real image formed in the imaging plane as the calibration point, extracts the corresponding initial frame index in the sequence image by matching the brightness peak value of the light spot in the microlens array image with the geometric focal position, and establishes a calibration point index set; The inter-frame peak extraction submodule extracts the image in the region where the calibration point is located in the microlens imaging image sequence according to the image frame position in the calibration point index set, obtains the two-dimensional gray scale distribution matrix of all the light spots in the image, detects the region where the gray scale value of the spot is greater than the set threshold value of the background gray scale value in the gray scale distribution graph, extracts the local maximum point position of the gray scale value in each frame of image as the peak coordinate, calculates the weighted peak coordinate of the calibration point light spot in all the frames of image, and obtains a peak coordinate sequence; The peak difference calculation sub-module performs position difference operation on the peak coordinate values of two adjacent frames according to all the continuous frame peak coordinate information in the peak coordinate sequence, extracts the horizontal and vertical difference values of two coordinate points respectively, takes absolute values, constructs an inter-frame peak coordinate difference matrix, and counts the position of the maximum difference point between the continuous frames, indexes and recombines all the difference coordinate information, and establishes a continuous coordinate sequence of the peak of the calibration point.

[0010] As a further scheme of the present application, the stability offset analysis module comprises: The three-dimensional offset extraction sub-module extracts the three-dimensional coordinate components of the calibration point between adjacent frames based on the coordinate data of each frame in the continuous coordinate sequence of the peak of the calibration point, obtains the coordinate difference values in the horizontal, vertical and depth directions, and obtains a sequence of inter-frame three-dimensional offset values; The tolerance judgment sub-module extracts the three-dimensional offset vector of each frame according to the offset values recorded in the sequence of inter-frame three-dimensional offset values, combines the offset mean value, offset standard deviation, vertical offset mean value and vertical offset standard deviation, calculates the stability offset abnormal index value of each frame, and marks the frame as abnormal if the value is greater than a preset reference threshold value, and obtains a frame abnormal state list; The sequence labeling sub-module performs abnormal and normal state identification coding processing on all frames in the order of frame number according to the frame abnormal state list, marks the abnormal frames as 1 and the normal frames as 0, establishes a state matrix combining the frame index and offset data, and constructs a continuous labeling structure of frame number-state in time sequence to obtain a spot stability offset labeling sequence.

[0011] As a further scheme of the present application, the system further comprises: The imaging quality determination module extracts the original image data of the microlens imaging area recorded by the corresponding image frame according to the abnormal frame number in the spot stability offset labeling sequence, selects the corresponding frame image as a detection reference frame in combination with the time position of the spot offset, and obtains a microlens microscopic imaging detection result by summarizing the registration condition of the abnormal spot and the image display area. The microlens microscopic imaging detection result comprises a reference image frame, spot abnormal area information and image offset registration data.

[0012] As a further scheme of the present application, the imaging quality determination module comprises: The frame image extraction sub-module extracts the frame numbers marked as abnormal states in the labeling sequence based on the abnormal frame number in the spot stability offset labeling sequence, extracts the image information of the microlens imaging area recorded by each corresponding frame, screens the imaging area gray array and pixel brightness value in the image matrix, arranges and stores the extracted image frames in ascending order according to the abnormal frame number, and generates an abnormal frame image set. The reference frame positioning sub-module compares the spatial variation of the corresponding light spot offset of each frame based on the time stamp and spatial position index corresponding to each image frame in the abnormal frame image set, extracts the brightness of the imaging center region and the gray value distribution of the edge region of the image frame, combines the time position corresponding to the frame number, judges the offset concentration degree and position stability characteristics of each image frame, selects a reference frame, and obtains a detection reference frame number; The registration analysis sub-module compares the brightness information, edge features and pixel arrangement of all image frames in the abnormal frame image set based on the detection reference frame number and the corresponding image as a reference standard image, analyzes the spatial variation of each abnormal frame on the imaging area profile and the matching offset degree of the center point, extracts the spatial distortion and boundary offset data of the local region of the image frame, collects the registration information of all image frames, and obtains the microlens microscopic imaging detection result.

[0013] Compared with the prior art, the advantages and positive effects of the present application are that: In the present application, by performing numerical aperture normalization processing on the direction of light passing through a specific aperture region, the spatial distribution characteristics of the light beam are accurately controlled, and on the basis of unifying the direction of light, the spatial mapping of the focal point position is completed, the direction consistency and imaging stability in the image forming process are improved, and combined with the continuous peak value coordinate search of the focal position of the light spot, the variation trend of the light spot can be dynamically obtained, the imaging deviation caused by the slight displacement can be effectively distinguished, and by means of absolute difference value extraction and Rayleigh criterion tolerance judgment of three-dimensional offset data, an accurate marking mechanism for the stability of the light spot is constructed, and the response sensitivity and analysis reliability of the weak instability factors in the imaging performance of the microlens are improved. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 The system flowchart of the present application is shown in the figure; Figure 2 The light source path establishment module flowchart of the present application is shown in the figure; Figure 3 The initial image position acquisition module flowchart of the present application is shown in the figure; Figure 4 The peak value coordinate recording module flowchart of the present application is shown in the figure; Figure 5 The stability offset analysis module flowchart of the present application is shown in the figure; Figure 6 The imaging quality judgment module flowchart of the present application is shown in the figure. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0016] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, which is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0017] Please refer to Figure 1 A micro-lens detection microscopic imaging system comprises: The light source path establishment module obtains the light beam convergence point formed at the first condenser end under the continuous emission state of the light source, detects the spatial coordinate distribution of the light rays passing through the first aperture diaphragm at the convergence point, classifies the coordinates according to the boundary between the light-shielding and light-transmitting regions, normalizes the NA numerical aperture of the light beam falling into the light-transmitting region through the collimator direction parameter (performs the standard of microscope imaging), and obtains the collimated light-transmitting direction parameter group; The initial image position acquisition module projects all light rays with the same direction within the field diaphragm range and reflects the light rays through the single-surface mirror, captures the focusing point position of the reflected light rays at the second condenser (the focal point position detection uses the laser speckle positioning method of the contact optical sensor), records the geometric center coordinate information of the light spot formed at the focal point, and generates the focal position spatial projection group; The peak coordinate recording module selects the first aperture diaphragm real image formed on the objective lens back focal plane (in accordance with the laser scanning specification of the microscope) as the light spot calibration point, searches for the peak position of the light spot at the calibration point in each frame of the micro-lens imaging image sequence, records the peak coordinate information in the continuous frames, obtains the absolute value of the coordinate difference between any two frames, and obtains the peak continuous coordinate sequence of the calibration point; The stability offset analysis module extracts the three-dimensional offset absolute value data between adjacent peak coordinates in all frames based on the peak continuous coordinate sequence of the calibration point (three-dimensional offset measurement: phase offset interference method of the optical three-dimensional measurement system), judges whether the offset value falls within the Rayleigh criterion tolerance (executes the surface tolerance standard of optical elements), marks all abnormal frame numbers in the data sequence, and generates a marked sequence in chronological order to obtain the light spot stability offset marked sequence; The imaging quality determination module extracts the original image data of the microlens imaging area recorded by the corresponding image frame according to the abnormal frame number in the light spot stability offset marking sequence, selects the corresponding frame image as a detection reference frame in combination with the light spot offset time position, and obtains the microlens microscopic imaging detection result by summarizing the registration condition of the abnormal light spot and the image display area.

[0018] The collimated light transmission direction parameter set includes a spatial direction distribution record of light rays, a numerical aperture normalization parameter, and a direction normalization coordinate system; the focus position spatial projection set includes a light spot geometric center projection coordinate, a reflection offset parameter, and focus positioning spatial data; the calibration point peak continuous coordinate sequence includes a light spot peak three-dimensional coordinate change amount, an inter-frame displacement amplitude data, and a time sequence displacement feature; the light spot stability offset marking sequence includes an abnormal frame number, a three-dimensional offset abnormality identifier, and an offset abnormality time index; and the microlens microscopic imaging detection result includes a reference image frame, a light spot abnormal area information, and image offset registration data.

[0019] Please refer to Figure 2 The light source path establishment module includes: The light beam convergence extraction submodule collects the light beam divergence direction information and the emission intensity change value of the first condenser end based on the emission path formed under the continuous emission state of the light source, obtains an angle vector sequence and a unit energy identifier sequence of the emission path direction, identifies the position relationship of each direction vector in space, identifies the focusing trend in combination with the incident angle characteristics, screens the intersection area meeting the focusing characteristics, and generates a focusing convergence coordinate set. The emission path formed under the continuous emission state of the light source first arranges a two-dimensional detection array at the first condenser end to collect the light beam divergence direction information and the path energy identifier of different emission paths. Each detection unit records the vector included angle and the unit energy intensity of the corresponding direction, for example, sampling points The included angle of the detection position corresponding direction is The energy intensity is Subsequently, all direction vectors are extended according to their spatial trends, the intersection points of different paths in space are identified, the spatial focusing trend is judged through the included angle between the path intersection points, the included angle screening threshold is set to If the included angle of two paths is less than the value, it is judged as effective focusing, such as The included angles of are and , which meet the condition, and the intersection point is retained. Further, the focusing density is determined according to the offset value of each intersection point to the theoretical optical axis center point. The offset value represents the deviation degree of the intersection point in the ideal focusing area. In the example, The offset amount is , The offset amount is , the three groups of sample mean values are , if the focus point offset average value is less than , it is determined that it is an effective focusing area, and finally all the intersection points and their coordinate data that meet the focusing direction vector and the intersection offset threshold are integrated to generate a focusing convergence coordinate set.

[0020] Table 1 light transmission direction vector sampling table As shown in Table 1, the path sampling data , and all meet the double screening standards of an included angle less than and an offset less than , and meet the set focusing criteria.

[0021] The space traversal coordinate determination submodule traces each intersection point along the incident direction to the first aperture stop position based on the position information of all intersection points in the focusing convergence coordinate set, identifies the traversal state of the intersection point in combination with the boundary space definition characteristics of the aperture stop, classifies the light blocking and light transmission based on the relative relationship between the boundary position and the incident direction, extracts the spatial position and direction vector characteristics of the light transmission type intersection point, and generates a light transmission direction distribution set; Based on the position information of all intersection point paths in the focusing convergence coordinate set, the intersection point of the extension direction of each path intersection point coordinate with the position of the first aperture stop in the space is tracked, the aperture stop is set as a circular plane structure in the space, the center is set as position, the boundary radius is , and the Euclidean distance between the intersection point coordinate of each path direction and the center point is used as the basis for judgment, for example, the position coordinate of a certain path after passing through the aperture plane is , the distance is , which is less than the boundary radius , and it is determined to be a light transmission point; otherwise, if the coordinate of a certain path is , the distance is , which exceeds the boundary, and it is determined to be a light blocking point. Through traversal point calculation and spatial positioning operation on all paths, the path points and their direction vectors in all light transmission regions are extracted respectively to form a spatial vector set belonging to the light transmission region, and a light transmission direction distribution set is generated.

[0022] The normalized direction calculation submodule determines the incident matching state according to the spatial relationship between the direction features of each direction vector in the light transmission direction distribution set and the optical axis of the collimator, and classifies the direction information that meets the spatial pointing requirements into a unified direction standard system, and performs direction vector normalization processing to obtain a collimated light transmission direction parameter group. According to the light transmission direction distribution, the angle between the incident direction and the optical axis of the collimator is further determined, and the optical axis of the collimator is set as -axial direction, the angle calculation method is the light transmission vector and the unit vector of the collimator axis The angle between them is For example, if a light transmission direction vector is The angle is The normalized criterion threshold is set to The direction with an angle greater than is excluded, such as The angle is It does not enter the collimation range, and is It meets the collimation condition. All light transmission directions that meet the normalized direction criterion are further unified into a reference direction coordinate system, with the optical axis direction of the collimator as the reference, and all deviated directions are mapped to a unified expression through angle offset, such as The original direction offset is The unified expression is The offset relative to the collimator axis direction, the direction normalization process is finally completed, and the collimated light transmission direction parameter group is output.

[0023] Please refer to Figure 3 , the initial image position acquisition module includes: The directional light projection submodule extracts the spatial pointing characteristics of each direction vector based on all light transmission direction vectors in the collimated light transmission direction parameter group, identifies whether it is located within the spatial angle range allowed by the field stop, filters out the direction data that conflicts with the field stop boundary, retains the direction information that can form a projection path within the effective projection range, and projects all directional light within the field stop range to generate a field direction projection set. Based on all light transmission direction vectors in the collimated light transmission direction parameter group, the unit vector characteristics of each direction vector in the spatial coordinate are first identified, and the consistency of the projection path and the spatial direction is determined by extracting the angle between the unit vector and the optical axis. For direction vectors with an angle range of to , they are considered as visible projection path candidates, and the projection of these paths on the field stop projection plane is determined. In the structural design, the field stop boundary radius is set to , if the lateral offset of the direction vector extension path and the center point of the diaphragm is less than the set radius, it is determined that the path is valid, and through this screening process, the direction path that meets the spatial angle and boundary range is identified, and then combined with the direction vector group of the path in the three-dimensional space, the direction uniform mapping processing is performed on all paths that meet the conditions, that is, the direction vectors of different sources are uniformly pointed to the standard reference axis through direction rotation transformation, and the transformation operation is realized by constructing the reference direction axis and performing spatial rotation projection on each path direction vector, thereby forming a path group with consistent direction in space, which is used as the available projection set in the field of view for the next stage of reflection processing path generation; Table 2 Direction vector space projection sample table As shown in Table 2, the included angles of samples D01, D02, D04 and D05 are , , , , all within the projection angle screening range, and their paths pass through the diaphragm region to form effective incidence, while the included angle of D03 is , which exceeds the set limit, and its path conflicts with the diaphragm boundary in extension, so it does not belong to the category of available paths. After the direction paths finally identified are uniformly arranged according to the standard direction, the field of view direction projection set is formed.

[0024] The reflection focus capture submodule identifies the intersection position with the single-surface mirror in space based on the direction path information in the field of view direction projection set, extracts the spatial angle relationship between the corresponding incident path and the reflection surface, confirms the reflection direction of each path according to the light path extension rule, marks the expected position region focused at the second condenser, judges the concentration region through the intersection trend of the reflection path, and detects the concentration region to extract the path convergence distribution of each focusing region and obtain the focal center point position formed by the reflection path, and establish the reflection focusing position group; According to all path information in the field of view direction projection set, the intersection position between each path and the single-surface mirror in space is identified, first set the mirror plane at the height of , the mirror normal vector is set as the direction perpendicular to the XY plane, that is , for sample number D01, the path direction unit vector is , the collimating mirror exit point is , the incident path intersection with the reflection surface is , the corresponding projection point coordinates are ; ; The incident intersection is obtained as , which is used to construct the included angle between the incident direction and the normal, and the calculation result is about , which meets the requirement of mirror reflection. After path reflection, it continues to extend and intersects with the focal plane of the second condenser (located at ). The intersection point of the reflected direction after extension is . It forms a spatial overlap area with other direction samples, for example, the focal point of sample D02 after reflection is , D04 is , and the focal points of the three in the Z-axis direction are all concentrated in the range of , with a lateral offset of no more than . This area is identified as a concentrated overlap area of the reflection path. The laser spot detection module is deployed within this range. By detecting the gray extreme point and the diffusion boundary of the spot, the gray maximum value area radius of D01 focal spot is measured as , D02 is , and D04 is . Their overlapping areas collectively cover about of the focal spot area. The final extracted center point coordinates of the spot are about , which is used as the representative of this area to establish the reflection focal position group.

[0025] The spot coordinate calculation submodule analyzes the geometric boundary form of the corresponding spot area according to the extracted spatial point data in the reflection focal position group, and identifies the intensity change center in each spot area. By tracking the change trend of the gray edge, the geometric center axis of the spot is extracted, the geometric center position of each spot area is identified, and all geometric center position information is integrated to obtain the focal position space projection group. According to all the focal point data identified in the reflection focal position group, the boundary recognition processing is performed on the spot image formed by each point, for example, the spot image formed by sample D01 on the focal plane is approximately elliptical structure, the boundary gray level gradually increases from the outside to the inside, and the spot outline transitions from to . By image sampling, the closed contour line with gray level greater than is extracted to form the outline boundary curve. Along the boundary line, the gray gradient change is calculated, and it is found that the spot has a directional diffusion characteristic, with a stable increasing trend in the long axis direction and a slow decay in the short axis direction. On this basis, the main axis of the gray distribution is extracted, with a length of about and a short axis of . The maximum gray point in the central region is located about above the midpoint of the main axis. The point is set as the candidate center, the main axis deviation angle is corrected by about , and the geometric center coordinates of the spot are obtained as after re-fitting the center point. The converted three-dimensional coordinates are , repeat the above process for sample D02 and D04 operation, the center point is 、 , the integration of the above point, unified projection into the same coordinate system, merge to generate a focus position space projection group.

[0026] Please refer to Figure 4 , the peak coordinate recording module includes: Aperture calibration submodule based on the focus position space projection group recorded in all the light spot coordinate data, extraction of projection points to meet the requirements of the objective lens back focal plane range, and judge the matching degree with the laser scanning structure, get the relative imaging plane position of the projection position, screening the first aperture diaphragm real image corresponding to the light spot position as a calibration point, by matching the brightness peak value of the light spot in the microlens array image and the geometric focus position, extract the corresponding initial frame index in the sequence image, establish the index set of calibration points; Based on the focus position space projection group recorded in all the light spot coordinate data, first, the three-dimensional coordinates of each light spot are extracted, and the Z-axis coordinate is used to judge whether it is in the microscope objective back focal plane interval. In order to ensure the imaging clarity, the focal plane is set to 200 μm, and the tolerance is ± 5 μm, so if the Z value is between 195 μm and 205 μm, it can be considered to meet the focal plane judgment standard. For example, the coordinates of S01 are (20.4, 15.8, 200), the coordinates of S02 are (21.0, 15.2, 202), and the coordinates of S04 are (22.1, 14.7, 199). The above three are located in the effective focal plane interval and are judged as valid light spot points. The Z-axis coordinates of S03 and S05 are 215 μm and 230 μm respectively, both of which exceed the upper limit boundary and are excluded. Then it is necessary to judge whether the light spot is located in the plane corresponding to the laser scanning path. For valid points, further map their spatial positions to imaging image frames to obtain their image frame numbers in the microlens imaging sequence, forming a structural index relationship. For example, S01 corresponds to frame number 12, S02 corresponds to frame number 14, and S04 corresponds to frame number 10. Record in the index table for subsequent frame image tracking operation. All light spots that meet the focal plane and path requirements will be included in the subsequent peak tracking objects, and the index set of calibration points is established. Table 3 calibration point selection and frame mapping table As shown in Table 3, S01, S02 and S04 are screened by Z-axis and mapped to valid frame index, while S03 and S05 are excluded.

[0027] The inter-frame peak extraction submodule extracts the image region where the calibration point is located in the microlens imaging image sequence according to the corresponding image frame position in the calibration point index set, obtains a two-dimensional gray distribution matrix of all the spots in the image, and detects the region in the gray distribution graph where the gray value of the spot is greater than the background gray value set threshold. The position of the local maximum point of the gray value in each frame image is extracted as the peak coordinate, and the formula is as follows: ; The weighted peak coordinates of the calibration point spot in all frame images are obtained by operation, and a peak coordinate sequence is obtained, wherein, represents the weighted peak coordinates of the calibration point spot in the i-th frame image, and the first term is the horizontal coordinate component, and the second term is the vertical coordinate component; represents the gray value of the j-th pixel point in the i-th frame image, represents the gray value of the j-th pixel point in the i-th frame image, represents the gray value of the j-th pixel point in the i-th frame image, represents the gray value of the j-th pixel point in the i-th frame image, represents the gray value of the j-th pixel point in the i-th frame image, represents the gray value of the j-th pixel point in the i-th frame image, represents the gray value of the j-th pixel point in the i-th frame image, represents the gray value of the j-th pixel point in the i-th frame image, represents the gray value of the j-th pixel point in the i-th frame image, represents the gray value of the j-th pixel point in the i-th frame image, According to the frame index recorded in the calibration point index set, the images numbered 10, 12 and 14 are read one by one, and the gray matrix of the region where S01 is located in the 12th frame is extracted. The background gray threshold is set to 45 gray, that is, the pixels below the threshold do not participate in the calculation of the gravity center. The weight of the pixels with a gray value greater than the threshold is extracted, and the absolute value of the difference between the pixel gray value and the average gray value is used as the weight. The weight is multiplied by the corresponding pixel position coordinates to obtain the weighted sum term. It is assumed that the gray values of the n=5 pixels participating in the calculation in the image are I1=120, I2=132, I3=115, I4=127 and I5=130, and the corresponding coordinates are x1=12, x2=13, x3=14, x4=15 and x5=16, y1=20, y2=19, y3=20, y4=21 and y5=20. The average gray value of the image is: ; The weighted deviation term of each pixel point is calculated: ; The weighted deviation term of each pixel point is calculated: Horizontal: ; Vertical: ; Weight sum: ; The weighted barycenter coordinate of frame 12 is finally calculated as: ; The coordinates of frame 10 (S04) and frame 14 (S02) are calculated in the same way, and the peak value coordinates of each frame are recorded in sequence to obtain a peak value coordinate sequence.

[0028] The weighted peak value coordinate refers to a two-dimensional spatial coordinate obtained by weighting the spatial positions of each pixel point in the image by the gray level deviation, which essentially represents the position where the energy or brightness of the light spot region is most concentrated in the current image frame. Unlike simply taking the maximum gray point or the geometric center of the image, the weighted peak value coordinate considers both the pixel gray intensity and the spatial distribution characteristics, and uses the gray level variation amplitude as the weight affecting the coordinate calculation, so that the influence of outlier pixels and edge noise is suppressed, while the regions with significant gray level and dense distribution have higher weight, thus dominating the determination of the center position. Therefore, this coordinate point can more stably and accurately reflect the real spatial barycenter position of the light spot, and has good representativeness and consistency in microlens imaging, optical tracking and spot positioning.

[0029] The formula is based on the calculation principle of the weighted geometric center, and the core goal is to measure the contribution of each pixel to the overall peak position through the difference between the gray level of each pixel in the image and the average gray level of the entire image. First, the absolute value of the difference between the gray value and the average value is used as the weight of the pixel, in order to highlight the pixels with large brightness dispersion, i.e. the pixels that significantly deviate from the average level in brightness. These regions often correspond to the energy concentration area of the light spot, and therefore are more representative. Then, the weight is multiplied by the position coordinates of the pixel in the horizontal and vertical coordinates, in order to emphasize the influence of the spatial position through weighting, and form a weighted barycenter that is biased towards the brightness aggregation area in space. The product of all participating pixels is accumulated through summation operation to form the numerator item of the weighted position, and the sum of the gray weights of all pixels is summed as the denominator item for normalization, to avoid bias caused by different number of participating pixels or overall gray level shift. Finally, the numerator is divided by the denominator to obtain the weighted coordinate center of the light spot in the region. Since the coordinate components are calculated separately, the confusion of horizontal and vertical dimensions is avoided, and the output result is a two-dimensional coordinate point used to represent the peak position of the calibration point in the current image frame. This process does not require nonlinear transformations such as square root and power, and maintains the geometric symmetry of the light spot and the spatial consistency of the image.

[0030] The peak difference calculation sub-module calculates the position difference of the peak coordinate values of two adjacent frames according to all the continuous frame peak coordinate information in the peak coordinate sequence, respectively extracts the horizontal and vertical difference values of the two coordinate points and takes the absolute value, constructs the inter-frame peak coordinate difference matrix, and counts the position of the maximum difference point between the continuous frames, indexes and reorganizes all the difference coordinate information, and establishes the continuous coordinate sequence of the peak of the calibration point; According to the results of frame 10, frame 12 and frame 14 in the peak coordinate sequence, the coordinate difference between the continuous frames is calculated. Assuming that the coordinates of frame 10 are (13.25, 19.20), the coordinates of frame 12 are (13.86, 19.82), and the coordinates of frame 14 are (14.10, 20.43), the inter-frame difference is calculated as follows: the horizontal difference between frame 10 and frame 12 is |13.86-13.25|=0.61, the vertical difference is |19.82-19.20|=0.62, the horizontal difference between frame 12 and frame 14 is |14.10-13.86|=0.24, and the vertical difference is |20.43-19.82|=0.61. The inter-frame difference vector list is arranged, and the maximum value point of the difference vector is marked in the difference sequence structure. For example, the 0.62 vertical change between frame 10 and frame 12 is the current maximum value, which is written in the index mark for subsequent trend judgment or filtering processing. Finally, the continuous coordinate sequence of the peak of the calibration point is obtained.

[0031] Please refer to Figure 5 The stability offset analysis module includes: The three-dimensional offset extraction sub-module extracts the three-dimensional coordinate components of the calibration point between adjacent frames based on the coordinate data of each frame in the continuous coordinate sequence of the peak of the calibration point, obtains the coordinate difference values in the horizontal, vertical and depth directions, and obtains the inter-frame three-dimensional offset value sequence; Based on the coordinate data of each frame in the continuous coordinate sequence of the peak of the calibration point, the three-dimensional coordinate values of the calibration point in the image frames F01 to F04 are first obtained, and the X, Y and Z three-dimensional components are recorded as reference space position parameters. Among them, the coordinates of F01 are (1020.0, 980.0, 150.0), the coordinates of F02 are (1045.5, 985.3, 157.8), the coordinates of F03 are (1073.2, 990.8, 163.1), and the coordinates of F04 are (1101.7, 996.2, 169.4). Then, the three-dimensional offset difference values are calculated for the space positions between adjacent frames. The difference results are as follows: ΔX=25.5nm, ΔY=5.3nm, ΔZ=7.8nm for F01-F02, ΔX=27.7nm, ΔY=5.5nm, ΔZ=5.3nm for F02-F03, and ΔX=28.5nm, ΔY=5.4nm, ΔZ=6.3nm for F03-F04. The Euclidean distance of the three-dimensional difference is calculated, and the length formula is used for processing to obtain the length of F01-F02: ; The length of molds F02-F03 is: ; The length of molds F03-F04 is: ; The final 3D coordinates and offset differences between all frames are summarized as follows: Table 4. Three-dimensional coordinate sequence of calibration points As shown in Table 4, the three-dimensional spatial coordinate data of each frame calibration point have been obtained and the inter-frame three-dimensional offset modulus has been calculated. The inter-frame three-dimensional offset value sequence has been obtained.

[0032] The tolerance determination submodule extracts the 3D offset vector for each frame based on the offset values ​​recorded in the inter-frame 3D offset value sequence. It then combines the offset mean, offset standard deviation, longitudinal offset mean, and longitudinal offset standard deviation using the following formula: ; The stability offset anomaly index value of each frame is obtained through calculation. If it exceeds a preset reference threshold, it is marked as an abnormal frame. A list of abnormal frame states is then obtained. Indicates the first Frame offset anomaly index value, The difference in three-dimensional coordinates between adjacent frames. These are the mean and standard deviation of the three-dimensional offset modulus, respectively. These are the mean and standard deviation of the longitudinal offset, respectively. Based on the offset magnitude data and Z-axis offset obtained from the inter-frame 3D offset value sequence, their statistical mean and standard deviation are constructed for subsequent normalization processing. First, the magnitudes between the three frames are: F01-F02 27.18nm, F02-F03 28.73nm, and F03-F04 29.68nm. The average of the three values ​​is then obtained... ; Then calculate its standard deviation: ; The Z-axis offset values ​​are 7.8nm, 5.3nm, and 6.3nm respectively. The average value is... ; The standard deviation is: ; Then, substituting the above parameters into the formula, taking F01-F02 as an example, with a module length of 27.18nm and ΔZ=7.8nm, the calculation is as follows: ; Judgment threshold value The setting basis is the confidence interval of the statistical distribution of the combined normalized offset term, and in this case, the offset indicator is composed of two dimensionless terms, which are derived from the three-dimensional offset module length and the Z-axis direction offset normalization term, respectively. The normalized data approximately obeys the standard normal distribution, and under the condition that the indicator fluctuation is within 95%, the sum of the two terms is controlled within the interval where the two-item Z scores are lower than ±1.3, so the value of is set as the critical judgment value, which does not depend on the specific device type, but its numerical range will fluctuate up and down with the increase and decrease of the overall offset volatility , , which is manifested as the value moving up in a high fluctuation system and the value decreasing in a low noise system, ensuring that the indicator M_t has consistent adjustable range in different precision systems. If the judgment threshold value , due to , the corresponding frame between F01-F02 is marked as an abnormal frame, and the offset indicators of the remaining frames are calculated in the same way and compared with the threshold value. After sorting the judgment results, the frame abnormal state list is obtained.

[0033] The stability offset abnormal indicator is a numerical quantitative result for measuring whether the spatial position change of the calibration point between frames in the image sequence exceeds the normal fluctuation range. This indicator is composed of the normalized deviations of the three-dimensional offset module length and the Z-axis direction offset, and its value reflects the superimposed effect of the stability degree and the longitudinal displacement sensitivity of adjacent image frames in the overall geometric space. When the indicator value is small, it indicates that the position change between frames is within the stable distribution range set by the system, and when the value is large, it indicates that the spatial drift amplitude of the frame pair or frame segment is significantly abnormal relative to the normal state, which may be caused by vibration, thermal drift, imaging drift, etc. Therefore, this indicator can be used as an important criterion for judging whether the image sequence has abnormal displacement behavior, and after comparison with the threshold value, the specific abnormal frame points are marked.

[0034] The operation logic of the formula is based on the dual structure of spatial geometric offset and statistical normalization judgment. First, the internal square sum and square root operation are used to construct the Euclidean module length of the three-dimensional coordinate difference, that is, the square sum of reflects the overall displacement amplitude of adjacent frames in X, Y, and Z directions, and then the square root operation is used to restore it to the actual spatial distance, which represents the comprehensive spatial amplitude of the position change between frames. Then, the three-dimensional module length value is subtracted from the module length mean to obtain the deviation from the normal fluctuation center, and through The standard deviation is normalized so that the difference can be converted into a standardized degree of outlier, i.e., the Z-score, which measures its relative position in the overall distribution. The deviation term is operated on using absolute values ​​to eliminate the influence of the direction of deviation on the judgment result, retaining only the degree of deviation. Similarly, the second part separately calculates the difference along the Z-axis. Also adopt absolute value and The normalization method reflects the individual impact of Z-direction fluctuations on overall offset stability; finally, the two normalized dimensionless terms are directly added to construct a comprehensive evaluation index. It takes into account both overall spatial changes and vertically sensitive changes, so that the formula has both the ability to judge the statistical stability of the horizontal space and the ability to reflect the drift characteristics of the system in the optical axis direction.

[0035] The sequence labeling submodule encodes all frames according to the abnormal state list and in the order of frame number, marking abnormal frames as 1 and normal frames as 0. It establishes a state matrix by combining the frame index and offset data, and constructs a continuous labeling structure of frame number-state in chronological order to obtain the spot stability offset labeling sequence. Based on the calculation results of the above three frames, and integrating the frame numbers F01 to F03 with the judgment results, if the index value M... t If the value is greater than the threshold of 1.8, it is marked as 1; otherwise, it is marked as 0. Frames F01-F02 are abnormal and marked as 1. The modulus of F02-F03 and F03-F04 are 28.73nm and 29.68nm, respectively, and the ΔZ is 5.3 and 6.3nm, respectively. Substituting the parameters and performing similar calculations, it is found that their indicators are all below the threshold and are marked as 0. The final relationship between frames and states is as follows: Table 5. Anomaly Labeling Table for Spot Stability Frames Referring to Table 5, the frame sequence number and status marker are integrated to construct a time series of anomaly identifiers and obtain the spot stability offset label sequence.

[0036] Please see Figure 6 The imaging quality assessment module includes: The frame extraction submodule extracts all frame numbers marked as abnormal states in the labeling sequence based on the abnormal frame number in the spot stability offset labeling sequence, extracts the microlens imaging area image information recorded in each corresponding frame, filters the imaging area grayscale array and pixel brightness value in the image matrix, and merges and stores the extracted image frames in ascending order according to the abnormal frame number to generate an abnormal frame image set. Based on the spot stability offset, the abnormal frame sequence number in the label sequence is marked, and the frame numbers F01, F03, F05 and F08 with mark state 1 are subjected to data screening operation, and the corresponding image data source of each frame is identified one by one, and the image frame storage path and image matrix corresponding to the number are extracted, the microlens imaging area is selected in the image matrix, the channel separation processing is carried out on the gray matrix, the 8-bit image structure with pixel point brightness value range of 0 to 255 is obtained, the time sequence sorting is carried out combined with the frame number corresponding timestamp, the image frame F01 time is 105ms, F03 is 117ms, F05 is 129ms, F08 is 144ms, the image frame sorting sequence is formed in ascending order of time, the image gray mean value and brightness edge distribution value are further extracted, which are F01: 120.5 and 80.2, F03: 118.2 and 76.8, F05: 115.9 and 74.3, F08: 119.4 and 78.5, then the pixel peak value area in the image matrix is identified, the imaging center brightness of each frame image is marked and stored, which are F01: 135.1, F03: 132.5, F05: 130.7, F08: 134.2, and the frame identification and image index binding structure are called at the same time to carry out image splicing operation, all frames are merged according to the frame number sequence to generate image collection dataset, and finally the abnormal frame image collection is obtained.

[0037] Table 6 Abnormal frame image attribute statistics table As shown in Table 6, the abnormal frame image collection has completed the gray scale, brightness, time sequence and other attribute extraction operation.

[0038] The reference frame positioning submodule compares the spatial variation of each frame corresponding to the spot offset based on the time stamp and spatial position index corresponding to each image frame in the abnormal frame image collection, extracts the imaging center area brightness and edge area gray value distribution of the image frame, combines the time position corresponding to the frame number, judges the offset concentration degree and position stability characteristics of each image frame, selects the reference frame, and obtains the detection reference frame number; According to the image frame properties extracted from the abnormal frame image set, the corresponding time stamp, gray mean value, imaging center brightness and spatial offset information of each frame are obtained. First, the time stamp data of the image frames with frame numbers F01, F03, F05 and F08 are called to analyze the time sequence difference of their offset behaviors. Then, the offset amounts in X and Y directions of each image frame are obtained. F01 has an X offset of 4px and a Y offset of 5px, F03 has an X offset of 3px and a Y offset of 6px, F05 has an X offset of 5px and a Y offset of 4px, and F08 has an X offset of 2px and a Y offset of 3px. Based on the offset combination of each frame and the image brightness difference value, the inter-frame fluctuation stability is judged. By comparing the difference between the edge brightness and the imaging center brightness, the brightness difference values of each frame are obtained, which are F01 54.9, F03 55.7, F05 56.4 and F08 55.7. By comparing the brightness difference values in all frames, the frame with the lowest brightness difference value and the smallest spatial offset combination is F08. This frame is marked and its number is recorded as the reference benchmark. Finally, the detection reference frame number is obtained.

[0039] Table 7 Image offset and brightness difference statistical table As shown in Table 7, by comparing the image frame brightness difference value and the offset amount, the detection reference frame number is selected as F08.

[0040] The registration analysis submodule compares the brightness information, edge features and pixel arrangement of all image frames in the abnormal frame image set according to the detection reference frame number and the corresponding image as the reference standard image, analyzes the spatial variation of each abnormal frame on the imaging area profile and the matching offset degree of the center point, extracts the spatial distortion and boundary offset data of the local area in the image frame, collects the registration information of all image frames, and obtains the microlens microscopic imaging detection result; According to the detection reference frame number F08, the image thereof is called as the registration control standard image, the pixel arrangement and brightness value distribution of the remaining image frames in the abnormal frame image set are compared, the edge region and center region pixel gray value in the F01, F03 and F05 image matrices are obtained, the brightness region variation and the pixel offset value of the corresponding region of the control frame are extracted, the offset length of the image profile line in X and Y directions in each frame is compared, the region offset amount is collected and sorted, then the distortion degree of the overall boundary region of the image is extracted, the number of edge connection feature points between the distorted region in the image and the control frame is compared by identifying the boundary extension distance variation and the region shape variation degree, and the number of dense projection points in the abnormal region in the image frame is obtained, which is 8 for F01, 7 for F03 and 6 for F05. Finally, the offset position difference, projection point density and gray space difference information of each frame are integrated, the overall registration feature data of the image frame is summarized, and the microlens microscopic imaging detection result is obtained.

[0041] Table 8 Image frame registration difference analysis table Referring to Table 8, the registration difference characteristic parameters between image frames have been sorted out and used for detection result judgment.

[0042] The above merely describes the preferred embodiments of the present application, but not other forms of the present application, any skilled person in the art can use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes applied to other fields, but any simple modification, equivalent change and modification made to the above embodiments without departing from the technical solution content of the present application, according to the technical essence of the present application, still belongs to the protection scope of the technical solution of the present application.

Claims

1. A microscopic imaging system for detecting microlenses, characterized in that, The system includes: The light source path establishment module obtains the beam convergence point formed by the light source at the first condenser lens end, detects the spatial coordinate distribution of the light passing through the first aperture stop at the convergence point, performs coordinate region classification processing, performs NA numerical aperture normalization on the beam falling into the light transmission region, and obtains the collimated light transmission direction parameter set. The initial image position acquisition module projects and reflects all rays with the same direction within the field of view aperture range according to the collimated light transmission direction parameter group, captures the focal point position of the reflected rays at the second condenser lens, records the coordinate information of the focal point, and generates a focal position spatial projection group. The peak coordinate recording module selects the spot calibration point according to the focused position spatial projection group and performs frame-by-frame peak position search on the spot at the calibration point, records the peak coordinate information, obtains the absolute value of the coordinate difference between any two frames, and obtains the continuous peak coordinate sequence of the calibration point. Based on the continuous coordinate sequence of the calibration point peaks, the stability offset analysis module extracts the absolute value data of the three-dimensional offset between adjacent peak coordinates in all frames, determines whether it falls within the Rayleigh criterion tolerance range, marks all abnormal frame numbers in the data sequence, and obtains the spot stability offset annotation sequence.

2. The microscopic imaging system for detecting microlenses according to claim 1, characterized in that, The collimated light transmission direction parameter set includes the spatial direction distribution record of light rays, numerical aperture normalization parameters, and direction normalization coordinate system. The focusing position spatial projection set includes the geometric center projection coordinates of the light spot, reflection offset parameters, and focal point positioning spatial data. The calibration point peak continuous coordinate sequence includes the three-dimensional coordinate change of the light spot peak, inter-frame displacement amplitude data, and time series displacement characteristics. The light spot stability offset annotation sequence includes the abnormal frame number, three-dimensional offset anomaly identifier, and offset anomaly time index.

3. The microscopic imaging system for detecting microlenses according to claim 1, characterized in that, The light source path establishment module includes: The beam convergence extraction submodule collects the beam divergence direction information and emission intensity change value at the first condenser lens end based on the emission path formed under the continuous emission state of the light source, obtains the angle vector sequence and unit energy identifier sequence of the emission path direction, identifies the positional relationship of the intersection of each direction vector in space, and identifies the focusing trend in combination with the incident angle characteristics, filters the intersection area that meets the focusing characteristics, and generates a focusing convergence coordinate set. The spatial crossing coordinate determination submodule tracks each intersection point along the incident direction to the position of the first aperture stop based on the position information of all intersection points in the focusing convergence coordinate set. It identifies the crossing state of the intersection point by combining the boundary space definition features of the aperture stop. It classifies light blocking and light transmission according to the relative relationship between the boundary position and the incident direction, extracts the spatial position and direction vector features of the light transmission intersection points, and generates a light transmission direction distribution set. The normalized direction calculation submodule determines the incident matching state based on the directional characteristics of each directional vector in the light transmission direction distribution set and the spatial relationship between them and the optical axis of the collimating lens. It then incorporates the directional information that meets the spatial pointing requirements into a unified directional standard system and performs directional vector normalization processing to obtain the collimated light transmission direction parameter set.

4. The microscopic imaging system for detecting microlenses according to claim 1, characterized in that, The initial image position acquisition module includes: The directional ray projection submodule extracts the spatial pointing characteristics of each directional vector based on all the transmission directional vectors in the collimated transmission directional parameter group, identifies whether it is within the spatial angle range allowed by the field stop, filters out directional data that conflict with the field stop boundary, retains directional information that can form a projection path within the effective projection range, and projects all rays with the same direction within the field stop range to generate a field directional projection set. The reflection focus capture submodule identifies the orientation of the intersection point with the single-sided mirror in space based on the directional path information of the field of view projection set, extracts the spatial angle relationship between the corresponding incident path and the reflecting surface, confirms the reflection direction of each path according to the light path extension rule, marks the expected focusing position area at the second condenser lens, judges the concentration area by the convergence trend of reflection paths, detects the concentration area, extracts the path convergence distribution of each focusing area, obtains the center point of the focus formed by the reflection path, and establishes a reflection focus position group. The spot coordinate calculation submodule analyzes the geometric boundary morphology of the corresponding spot region based on all spatial point data extracted from the reflection focusing position group, identifies the intensity change center in each spot region, extracts the geometric contour central axis of the spot by tracking the change trend of grayscale edge, identifies the geometric center position of each spot region, integrates all geometric center position information, and obtains the focusing position spatial projection group.

5. The microscopic imaging system for detecting microlenses according to claim 1, characterized in that, The peak coordinate recording module includes: The aperture calibration submodule extracts projection points that meet the requirements of the objective lens back focal plane range based on all the light spot coordinate data recorded in the focal position spatial projection group, and determines the degree of matching with the laser scanning structure. It obtains the relative imaging plane position of the projection position, selects the light spot position corresponding to the real image of the first aperture stop formed in the imaging plane as the calibration point, and extracts the corresponding initial frame index in the sequence image by matching the brightness peak and geometric focus position of the light spot in the microlens array image, and establishes a calibration point index set. The inter-frame peak extraction submodule extracts the image of the region where the calibration point is located in the microlens imaging image sequence according to the image frame position corresponding to the calibration point index set, obtains the two-dimensional gray-level distribution matrix of all light spots in the image, and detects the region in the gray-level distribution map where the gray value of the spot is greater than the background gray value set threshold. The local maximum point position of gray value in each frame image is extracted as the peak coordinate, and the weighted peak coordinates of the calibration point light spots in all frame images are calculated to obtain the peak coordinate sequence. The peak difference calculation submodule performs position difference calculation on the peak coordinate values ​​of two adjacent frames based on the peak coordinate information of all consecutive frames in the peak coordinate sequence. It extracts the horizontal and vertical differences of the two coordinate points and takes the absolute value to construct the inter-frame peak coordinate difference matrix. It also counts the position of the largest difference point between consecutive frames, indexes and reorganizes all difference coordinate information, and establishes a continuous peak coordinate sequence of calibration points.

6. The microscopic imaging system for detecting microlenses according to claim 1, characterized in that, The stability migration analysis module includes: The three-dimensional offset extraction submodule extracts the three-dimensional coordinate components of the calibration points between adjacent frames based on the coordinate data of each frame in the continuous coordinate sequence of the calibration point peaks, and obtains the coordinate differences in the horizontal, vertical and depth directions to obtain the inter-frame three-dimensional offset value sequence. The tolerance judgment submodule extracts the three-dimensional offset vector of each frame based on the offset values ​​recorded in the inter-frame three-dimensional offset value sequence. It calculates and obtains the stability offset anomaly index value of each frame by combining the offset mean, offset standard deviation, longitudinal offset mean, and longitudinal offset standard deviation. If the value is greater than the preset reference threshold, it is marked as an abnormal frame and a list of abnormal frame states is obtained. The sequence labeling submodule encodes all frames according to the frame abnormal state list, in order of frame number, and marks abnormal frames as 1 and normal frames as 0. It establishes a state matrix by combining the frame index and offset data, and constructs a continuous labeling structure of frame number-state in chronological order to obtain the spot stability offset labeling sequence.

7. The microscopic imaging system for detecting microlenses according to claim 1, characterized in that, The system also includes: The imaging quality determination module extracts the original image data of the microlens imaging area recorded in the corresponding image frame according to the abnormal frame number in the spot stability offset labeling sequence, and selects the corresponding frame image as the detection reference frame in combination with the spot offset time position. It then summarizes the registration of abnormal spots and image representation areas to obtain the microlens microscopic imaging detection results. The microlens microscopic imaging detection results include reference image frames, information on abnormal light spot regions, and image offset registration data.

8. The microscopic imaging system for detecting microlenses according to claim 7, characterized in that, The imaging quality determination module includes: The frame extraction submodule extracts all frame numbers marked as abnormal in the labeling sequence based on the abnormal frame number in the spot stability offset labeling sequence, extracts the microlens imaging area image information recorded in each corresponding frame, filters the imaging area grayscale array and pixel brightness value in the image matrix, and merges and stores the extracted image frames in ascending order according to the abnormal frame number to generate an abnormal frame image set. The reference frame localization submodule compares the spatial changes of the spot offset corresponding to each image frame in the abnormal frame image set based on the timestamp and spatial location index of each image frame, extracts the brightness distribution of the imaging center region and the gray value distribution of the edge region of the image frame, and judges the degree of offset concentration and position stability characteristics of each image frame by combining the time position corresponding to the frame number, selects the reference frame, and obtains the detection reference frame number. The registration analysis submodule, based on the detection reference frame number and the corresponding image as a reference standard image, compares the brightness information, edge features, and pixel arrangement of all image frames in the abnormal frame image set. It analyzes the spatial changes and center point matching offset of each abnormal frame on the imaging area contour, extracts the spatial distortion and boundary offset data of local areas in the image frames, collects the registration information of all image frames, and obtains the microlens microscopic imaging detection results.

Images