Image scanning microscopic super-resolution reconstruction method and device based on frequency decomposition

Abstract

The invention relates to the technical field of optical imaging and image processing, and discloses an image scanning microscopic super-resolution reconstruction method and device based on frequency decomposition, and the method comprises the steps: obtaining an original image data block set; wherein the original image data block set comprises a plurality of original dot matrix images obtained by performing two-dimensional scanning on a target sample by using an image scanning microscopic system; performing multi-stage frequency decomposition on the original dot matrix image, and filtering out the highest-frequency noise component to obtain a plurality of two-dimensional frequency components; performing fusion processing on the plurality of two-dimensional frequency components to generate a binary digital pinhole mask; and based on the binary digital pinhole mask and the highest two-dimensional frequency component in the plurality of two-dimensional frequency components, performing high-frequency information extraction and synthesis to obtain a super-resolution reconstructed image. The invention provides a faster, more stable, simpler and more convenient super-resolution imaging tool.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of optical imaging and image processing, in particular to an image scanning microscopic super-resolution reconstruction method and device based on frequency decomposition. BACKGROUND

[0002] The image scanning microscopic imaging technology is a new super-resolution fluorescence microscopic technology, which can not only break the limit of optical diffraction limit on imaging resolution, but also has strong tissue penetration ability and tomographic imaging ability.

[0003] At present, the super-resolution reconstruction method based on the image scanning microscopic technology adopts a pixel reassignment algorithm, but the algorithm has problems of slow processing speed, sensitivity to signal-to-noise ratio, complex process and insufficient information utilization, which leads to low super-resolution reconstruction quality and limits the further improvement of resolution. SUMMARY

[0004] The present application provides an image scanning microscopic super-resolution reconstruction method and device based on frequency decomposition to solve the problems of slow processing speed, sensitivity to signal-to-noise ratio and complex process of related super-resolution reconstruction methods.

[0005] In a first aspect, the present application provides an image scanning microscopic super-resolution reconstruction method based on frequency decomposition, which comprises: obtaining an original image data block set, wherein the original image data block set comprises a plurality of original dot array images obtained by two-dimensional scanning of a target sample by an image scanning microscopic system; performing multi-level frequency decomposition on the original dot array images to filter out the highest frequency noise component and obtain a plurality of two-dimensional frequency components; performing fusion processing on the plurality of two-dimensional frequency components to generate a binary digital pinhole mask; performing high-frequency information extraction and synthesis based on the binary digital pinhole mask and the highest two-dimensional frequency component in the plurality of two-dimensional frequency components to obtain a super-resolution reconstruction image.

[0006] The application provides a frequency decomposition-based image scanning microscopic super-resolution reconstruction method, which decomposes an original dot array image into a plurality of two-dimensional frequency components by performing multi-stage frequency decomposition on the original dot array image, and performs fusion processing on the plurality of two-dimensional frequency components to obtain a binary digital pinhole mask, so that the standardized process of frequency decomposition-fusion-mask greatly reduces the requirement for the professional background of an operator, improves the repeatability and ease of use of the method, and expands the application scenarios, and based on the binary digital pinhole mask and the highest two-dimensional frequency component in the plurality of two-dimensional frequency components, the complete high-frequency component after adaptive mask processing is directly extracted and reserved, so that the photon information collected by the detector is more fully utilized, thereby theoretically having the potential to excavate higher limit resolution, and through the technical path of frequency separation and parallel processing, the direct super-resolution image synthesis without sub-pixel positioning, without digital pinhole and without Fourier transform is realized, the processing speed of super-resolution imaging is improved, and the super-resolution reconstruction image has high signal-to-noise ratio and high resolution.

[0007] In an optional embodiment, the original dot array image is subjected to multi-stage frequency decomposition, and the highest frequency noise component is filtered out to obtain a plurality of two-dimensional frequency components, including: The original dot array image is subjected to frequency screening to obtain a candidate modal function; The original dot array image is subtracted from the candidate modal function to obtain a residual component; The residual component is subjected to iterative decomposition to obtain a plurality of modal functions; The plurality of modal functions are sorted from high to low in frequency to obtain a plurality of modal components; The highest frequency noise component is extracted from the plurality of modal components, and the highest frequency noise component is filtered out to obtain a plurality of two-dimensional frequency components.

[0008] The application provides a frequency decomposition-based image scanning microscopic super-resolution reconstruction method, which separates modal components from high frequency to low frequency in the original dot array image through iterative screening, and finally obtains modal functions and residual components of different frequencies, follows the separation logic of high frequency first and low frequency later, and the first obtained modal component corresponds to the highest frequency information of the image, the subsequent components correspond to the medium frequency texture and the low frequency background in turn, and the final residual component is the stationary trend base of the image, the layered boundary is clear, the orderliness of frequency decomposition is ensured, and the local detail features of the original dot array image are maximally retained, thereby providing high-quality and layered clear basic data for subsequent component processing and super-resolution reconstruction.

[0009] In an optional embodiment, the original dot array image is subjected to frequency screening to obtain a candidate modal function, including: The original dot array image is initialized to obtain local extreme points; The local extreme points are interpolated by a surface to obtain an upper envelope surface and a lower envelope surface, and a mean envelope surface is calculated based on the upper envelope surface and the lower envelope surface; A difference between the original point array image and the mean envelope surface is calculated to obtain a candidate modal function; The candidate modal function is compared with a modal function condition, and if the candidate modal function satisfies the modal function condition, the candidate modal function is output.

[0010] The image scanning microscopic super-resolution reconstruction method based on frequency decomposition provided by the application does not need to perform complex Fourier transform, is directly completed through a specific envelope function, and retains effective high-frequency structure information in an image block.

[0011] In an optional embodiment, a highest frequency noise component is extracted from the plurality of modal components, and the highest frequency noise component is filtered out to obtain a plurality of two-dimensional frequency components, including: A structural similarity index between the plurality of modal components is calculated, and the highest frequency noise component is determined based on the structural similarity index; The highest frequency noise component in the plurality of modal components is separated and filtered out to obtain a plurality of two-dimensional frequency components.

[0012] The image scanning microscopic super-resolution reconstruction method based on frequency decomposition provided by the application quantifies modal component feature correlation through a structural similarity index, accurately distinguishes noise components and effective components, accurately separates the highest frequency noise component, and maximizes the retention of effective two-dimensional frequencies.

[0013] In an optional embodiment, the plurality of two-dimensional frequency components are fused to generate a binary digital pinhole mask, including: The plurality of two-dimensional frequency components are fused to obtain an intermediate image; Based on the intermediate image, a solid circular structure is identified and extracted by using a solid circular spot automatic detection algorithm, wherein the solid circular structure conforms to a diffraction limit spot feature; Pixel gray scale information corresponding to the solid circular structure is binarized to obtain a binary digital pinhole mask.

[0014] The application provides a frequency decomposition-based image scanning microscopic super-resolution reconstruction method, which performs component fusion on a plurality of two-dimensional frequency components to obtain an intermediate image, retains the morphological characteristics of a light spot, improves the signal-to-noise ratio, and effectively suppresses background noise, and the application uses a solid circular spot automatic detection algorithm to identify and extract a solid circular structure, accurately identifies a diffraction-limited light spot, reduces manual intervention, and improves processing speed; finally, the pixel gray scale information corresponding to the solid circular structure is subjected to binaryzation processing, and the solid circular structure is converted into a binary digital pinhole mask, and for light spots under different samples and different imaging conditions, the mask can automatically adjust the number and distribution of pinholes, without the need for manual parameter modification, the binary digital pinhole mask dynamically adapts to the characteristics of the light spot, supports super-resolution reconstruction, and solves the problems of poor image quality, difficult feature positioning, and poor adaptability of subsequent processing.

[0015] In an optional embodiment, based on the binary digital pinhole mask and the highest two-dimensional frequency component in the plurality of two-dimensional frequency components, high-frequency information extraction and synthesis are performed to obtain a super-resolution reconstruction image, including: Pixel-by-pixel multiplication is performed on the binary digital pinhole mask and the highest two-dimensional frequency component to obtain a super-resolution image block; Pixels corresponding to the super-resolution image block are mapped into a global grid in parallel to generate an initial super-resolution image; Image post-processing is performed on the initial super-resolution image to obtain a super-resolution reconstruction image.

[0016] The frequency decomposition-based image scanning microscopic super-resolution reconstruction method provided by the application directly extracts and retains complete high-frequency components after adaptive mask processing, more fully utilizes the photon information collected by the detector, and thus has the potential to theoretically excavate higher limit resolution.

[0017] In a second aspect, the application provides a frequency decomposition-based image scanning microscopic super-resolution reconstruction device, which includes: An acquisition module is configured to acquire an original image data block set, wherein the original image data block set includes a plurality of original dot array images obtained by performing two-dimensional scanning on a target sample by using an image scanning microscopic system; A multi-stage frequency decomposition module is configured to perform multi-stage frequency decomposition on the original dot array images to filter out the highest frequency noise components and obtain a plurality of two-dimensional frequency components; A fusion processing module is configured to perform fusion processing on the plurality of two-dimensional frequency components to generate a binary digital pinhole mask; An extraction and synthesis module is configured to perform high-frequency information extraction and synthesis based on the binary digital pinhole mask and the highest two-dimensional frequency component in the plurality of two-dimensional frequency components to obtain a super-resolution reconstruction image.

[0018] In a third aspect, the present application provides an electronic device, comprising a memory and a processor, the memory and the processor being communicatively connected with each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the frequency decomposition based image scanning microscopic super-resolution reconstruction method of the first aspect or any of the corresponding embodiments thereof.

[0019] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer instructions for causing a computer to perform the frequency decomposition based image scanning microscopic super-resolution reconstruction method of the first aspect or any of the corresponding embodiments thereof.

[0020] In a fifth aspect, the present application provides a computer program product, which comprises computer instructions for causing a computer to perform the frequency decomposition based image scanning microscopic super-resolution reconstruction method of the first aspect or any of the corresponding embodiments thereof. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed in the specific embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0022] Figure 1 is a schematic diagram of an application scenario according to an embodiment of the present application; Figure 2 is a first flowchart of a frequency decomposition based image scanning microscopic super-resolution reconstruction method according to an embodiment of the present application; Figure 3 is a second flowchart of a frequency decomposition based image scanning microscopic super-resolution reconstruction method according to an embodiment of the present application; Figure 4 is a third flowchart of a frequency decomposition based image scanning microscopic super-resolution reconstruction method according to an embodiment of the present application; Figure 5 is a fourth flowchart of a frequency decomposition based image scanning microscopic super-resolution reconstruction method according to an embodiment of the present application; Figure 6 is a flowchart of a super-resolution imaging algorithm based on Fourier transform and pixel reassignment according to an embodiment of the present application; Figure 7 is a whole flowchart of a frequency decomposition based image scanning microscopic super-resolution reconstruction method according to an embodiment of the present application; Figure 8is a flowchart of a frequency decomposition algorithm according to an embodiment of the present application; Figure 9 is a structural block diagram of an image scanning microscopic super-resolution reconstruction device based on frequency decomposition according to an embodiment of the present application; Figure 10 is a hardware structure schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] To make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0024] It can be understood that, before using the technical solutions disclosed in the embodiments of the present application, the type, use range, use scenario and the like of personal information involved in the present application should be informed to the user and the authorization of the user should be obtained through appropriate means according to relevant laws and regulations.

[0025] The terms "first", "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0026] As an optional application scenario of the embodiments of the present application, as shown in Figure 1 The image scanning microscopic super-resolution reconstruction system based on frequency decomposition can include at least one terminal device and at least one server, Figure 1 The system includes a computer 101, a mobile terminal 102 and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0027] The terminal device can be specifically a smart phone, a tablet computer, a notebook computer, a palm computer, or a desktop computer, a game console, a smart television, a smart wearable device, a vehicle-mounted terminal, a VR (Virtual Reality) device, an AR (Augmented Reality) device, or the like. The server 103 can be a standalone physical server, a server cluster, or a distributed system, or a cloud server providing cloud services. The network 110 can be a wired network or a wireless network, and examples thereof include, but are not limited to, the Internet, an intranet, a local area network, a wide area network, a mobile communication network, and a combination thereof.

[0028] Currently, the mainstream super-resolution reconstruction method based on image scanning microscopy adopts a pixel reassignment algorithm, which is implemented through the following steps: pixel reassignment is the mainstream super-resolution image reconstruction method based on image scanning microscopy, and the steps include: peak detection: performing spot peak detection on the detector array image corresponding to each scanning point; sub-pixel positioning: performing Gaussian fitting or centroid calculation on the detected spot to determine the center thereof with sub-pixel accuracy; applying a digital pinhole: using a Gaussian function as a virtual pinhole to weight the spot and suppress the background and out-of-focus light; pixel reassignment: repositioning the weighted sub-pixel point to a higher resolution grid; summation: accumulating the points falling into the same high-resolution grid pixel to generate the final super-resolution image.

[0029] The reconstruction method based on pixel reassignment has a core algorithm of point-by-point serial processing, which needs to sequentially position and reassign thousands of focal points, and has low calculation efficiency, cannot meet the real-time imaging requirements of dynamic processes of living cells, and the accuracy of sub-pixel positioning is seriously dependent on the signal-to-noise ratio of each focal spot. Under low light, low marker density, or fast imaging conditions, a decrease in the signal-to-noise ratio will cause positioning errors, introduce artifacts, and significantly reduce the reconstruction quality; the algorithm involves multiple parameters that need to be manually optimized, such as peak detection threshold, fitting algorithm, and digital pinhole size, and the process is complicated, has low repeatability, and requires high experience of the user; the digital pinhole suppresses the background while discarding the photons that may contain useful high-frequency information outside the spot, limiting the potential for further improvement of the resolution.

[0030] Therefore, there is an urgent need in the art for a new super-resolution image reconstruction method that can overcome the above-mentioned shortcomings, has high speed, high robustness, and a simple process.

[0031] To solve the problems of slow processing speed, sensitivity to signal-to-noise ratio, complex process and insufficient information utilization of the pixel reallocation technology, the embodiment of the present application provides a frequency decomposition-based image scanning microscopic super-resolution reconstruction method, and proposes a brand-new image scanning microscopic super-resolution reconstruction paradigm, which is different from the serial idea based on "point spread function positioning and reallocation". The embodiment of the present application realizes direct super-resolution image synthesis without sub-pixel positioning, without digital pinhole and without Fourier transform through the technical path of frequency separation and parallel processing. The technical scheme fundamentally solves the three core pain points of slow processing speed, sensitivity to signal-to-noise ratio and complex process of related technologies, and provides a faster, more stable and more convenient super-resolution imaging tool.

[0032] According to the embodiment of the present application, a frequency decomposition-based image scanning microscopic super-resolution reconstruction method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0033] In the embodiment, a frequency decomposition-based image scanning microscopic super-resolution reconstruction method is provided, which can be used in the terminal device described above. Figure 2 The flowchart of the frequency decomposition-based image scanning microscopic super-resolution reconstruction method according to the embodiment of the present application is shown in FIG. 1, which includes the following steps: Figure 2 As shown in FIG. 1, the flowchart includes the following steps: Step S201, acquiring an original image data block set; wherein the original image data block set includes a plurality of original dot array images obtained by two-dimensional scanning of a target sample by an image scanning microscopic system.

[0034] Specifically, the target sample is scanned by the image scanning microscopic system, and at each scanning position, a corresponding original image block set is acquired by the array detector, that is, the target sample is two-dimensionally scanned by the image scanning microscopic system, and at each scanning position , the array detector captures a corresponding two-dimensional light spot image, which is called an "image block", that is, an original dot array image, and the set of all image blocks constitutes an original data set .

[0035] The target sample can be a micro-scale biological or industrial material, for example, a case section sample, a cell sample, a semiconductor wafer, a fiber material, etc.

[0036] Step S202, performing multi-level frequency decomposition on the original dot array image to filter out the highest frequency noise component and obtain a plurality of two-dimensional frequency components.

[0037] Specifically, each image block in the original image block set is processed. Using frequency separation technology, the low-frequency background information and high-frequency noise components in each image block are directly separated and filtered out. This step does not require complex Fourier transform and is completed directly in the spatial domain or through a specific envelope function, thus preserving the effective mid-to-high frequency structural information in the image block.

[0038] Furthermore, for each image patch Perform multi-level frequency decomposition, dividing each image block Decomposed into frequencies from high to low. Two-dimensional frequency components In this decomposition process, the highest frequency component The highest frequency noise component (i.e., the most frequent noise component) mainly contains random noise, which is directly discarded in subsequent processing to achieve front-end denoising. The remaining components cover effective information from background to details.

[0039] Step S203: Multiple two-dimensional frequency components are fused to generate a binarized digital pinhole mask.

[0040] Step S204: Based on the binarized digital pinhole mask and the highest two-dimensional frequency component among multiple two-dimensional frequency components, high-frequency information is extracted and synthesized to obtain a super-resolution reconstructed image.

[0041] Specifically, the binarized digital pinhole mask The high-frequency components containing super-resolution details obtained in step S202 are applied. The above (generally the next level of two-dimensional frequency components after discarding the highest frequency noise component, such as...) The super-resolution image blocks are obtained by stacking the super-resolution image blocks corresponding to each original bitmap image in parallel to obtain the super-resolution reconstructed image.

[0042] The embodiment provides a frequency decomposition-based image scanning microscopic super-resolution reconstruction method, which decomposes an original dot array image into a plurality of two-dimensional frequency components through multi-stage frequency decomposition of the original dot array image, and performs fusion processing on the plurality of two-dimensional frequency components to obtain a binary digital pinhole mask. Through the standardized process of frequency decomposition-fusion-mask, the requirement for the professional background of an operator is greatly reduced, the repeatability and ease of use of the method are improved, the application scenarios are expanded, and based on the binary digital pinhole mask and the highest two-dimensional frequency component in the plurality of two-dimensional frequency components, the complete high-frequency component after adaptive mask processing is directly extracted and reserved, the photon information collected by the detector is more fully utilized, and therefore, the method theoretically has the potential to excavate higher limit resolution, realizes direct super-resolution image synthesis without sub-pixel positioning, without a digital pinhole and without Fourier transform through the technical path of frequency separation and parallel processing, improves the processing speed of super-resolution imaging, and makes the super-resolution reconstruction image have high signal-to-noise ratio and high resolution.

[0043] In the embodiment, a frequency decomposition-based image scanning microscopic super-resolution reconstruction method is provided, which can be used for the terminal device described above, Figure 3 is a flowchart of the frequency decomposition-based image scanning microscopic super-resolution reconstruction method according to the embodiment of the application, as Figure 3 shown, the flowchart comprises the following steps: Step S301, acquiring an original image data block set; wherein the original image data block set comprises a plurality of original dot array images obtained by two-dimensional scanning of a target sample by using an image scanning microscopic system. For details, refer to step S201 of the embodiment shown in Figure 2 not repeated here.

[0044] Step S302, performing multi-stage frequency decomposition on the original dot array image to filter out a highest frequency noise component to obtain a plurality of two-dimensional frequency components.

[0045] Specifically, the step S302 comprises the following steps: Step S3021, performing frequency screening on the original dot array image to obtain a candidate modal function.

[0046] The original dot array image is decomposed into a plurality of modal components from high frequency to low frequency by using two-dimensional empirical mode decomposition, and the two-dimensional empirical mode decomposition is a completely data-driven and adaptive decomposition process. The process of the two-dimensional empirical mode decomposition reflects the process of extracting the local highest frequency and the second highest frequency.

[0047] In some optional embodiments, the step S3021 comprises the following steps: Step a1, initializing the original dot array image to obtain local extreme points.

[0048] Specifically, the original dot matrix image is initialized, and local extreme points (maximum points and minimum points) are calculated, and the specific steps include: obtaining the coordinates and pixel values of each pixel in the original dot matrix image; selecting a neighborhood window according to the image resolution, extracting all effective pixel values in the neighborhood window for each pixel in the image; if the current pixel value is greater than or equal to all other effective pixel values in the neighborhood window, the current pixel value is marked as a local maximum point; otherwise, if the current pixel value is less than or equal to all other effective pixel values in the neighborhood window, the current pixel value is marked as a local minimum point; after traversing all pixels, a set of local extreme points is obtained.

[0049] Step a2, surface interpolation is performed on the local extreme points to obtain an upper envelope surface and a lower envelope surface, and a mean envelope surface is calculated based on the upper envelope surface and the lower envelope surface.

[0050] Specifically, surface interpolation is performed on the local maximum points and the local minimum points respectively to obtain the upper envelope surface and the lower envelope surface, and then the mean envelope surface is calculated, wherein the upper envelope surface and the lower envelope surface are consistent with the size of the original dot matrix image.

[0051] Step a3, the difference between the original dot matrix image and the mean envelope surface is calculated to obtain a candidate modal function.

[0052] Specifically, the difference between the original dot matrix image and the mean envelope surface is calculated to obtain the modal function, that is, the same pixel coordinates of the original dot matrix image and the mean envelope surface are subjected to subtraction operation to obtain the modal function.

[0053] Step a4, compare the candidate modal function with the modal function condition, if the candidate modal function meets the modal function condition, output the candidate modal function.

[0054] Specifically, it is judged whether the modal function meets the modal function condition: the screening range of the modal function (two-dimensional frequency component) is adjusted by the mean square error of two envelope fittings, and if the mean square error of two envelope fittings is less than a set threshold, the screening stops.

[0055] Step S3022, subtract the candidate modal function from the original dot matrix image to obtain a residual component.

[0056] Specifically, the above steps a1-a3 are repeated until the first modal function (i.e. the candidate modal function) is obtained which meets the given modal function condition, and the first residual component is obtained by subtracting the first modal function from the original image.

[0057] Step S3023, iteratively decompose the residual component to obtain a plurality of modal functions.

[0058] Specifically, the steps a1-a4 are repeated for the residual component to obtain modal functions in sequence.

[0059] Further, the termination condition of the iterative decomposition of the residual component is to determine the number of extreme points: whether the residual component contains at least 3 extreme points, if not, stop the decomposition process; otherwise, continue to repeat the steps a1-a4 to obtain the number of modal functions, which is the decomposition order .

[0060] Further, the fewer the number of extreme points of the residual component, the lower the frequency (tending to be a stationary background component), that is, when there are less than 3 extreme points, the envelope surface cannot be effectively fitted, and therefore the decomposition is stopped.

[0061] Further, based on the statistical characteristics of the local extreme points, the number of layers (i.e., the decomposition order) should be sufficient to cover the scale distribution range from high frequency to low frequency. Generally, when the last modal function after decomposition no longer contains obvious oscillation, it can be considered to reach a suitable level. In most cases, the initial value (such as 4-6 layers) can be set by experiment or experience, and the screening range of two-dimensional frequency components of each layer is adjusted by the mean square error of two envelope fittings. A smaller mean square error value will lead to more screening times, which may extract purer frequency components, but will lead to an increase in the number of decompositions, and the difference between two-dimensional frequency components of each layer is smaller. A larger mean square error value will lead to fewer screening times, which may not be pure enough, and some modal mixing will be left. In most cases, the mean square error value (such as 0.01-0.1) can be set by experiment or experience.

[0062] When the last residual term after decomposition no longer contains obvious oscillation, it is considered to reach a suitable level, and the value can be set in advance, and the initial value (such as 4-6) can be set by experiment or experience.

[0063] In step S3024, the plurality of modal functions are sorted from high to low in frequency to obtain a plurality of modal components.

[0064] Specifically, the plurality of modal functions are sorted from high to low in frequency, denoted as , and a residual component , and the result can be written as:

[0065] wherein, is the number of frequencies, i.e., the decomposition order.

[0066] Step S3025, extracting a highest frequency noise component from the plurality of modal components, and filtering out the highest frequency noise component to obtain a plurality of two-dimensional frequency components.

[0067] In some optional embodiments, the step S3025 comprises: Step b1, calculating a structural similarity index between the plurality of modal components, and determining the highest frequency noise component based on the structural similarity index.

[0068] Specifically, the determination of the highest frequency noise component comprises that the structural similarity index is an index for measuring the similarity between two images, and the structural similarity index is used to measure the similarity between the original dot array image and each modal component after decomposition , that is, by comparing the structural similarity index between each modal component , the determination of whether each modal component is a noise-dominant component or a signal-dominant component is determined; if the structural similarity index of the modal component is less than 0.90, it is considered that the modal component is a noise-dominant component, and the component needs to be discarded in subsequent execution.

[0069] Step b2, separating and filtering out the highest frequency noise component in the plurality of modal components to obtain a plurality of two-dimensional frequency components.

[0070] Step S303, fusing the plurality of two-dimensional frequency components to generate a binary digital pinhole mask. For details, please refer to step S203 of the embodiment shown in Figure 2 , which will not be described here again.

[0071] Step S304, based on the binary digital pinhole mask and the highest two-dimensional frequency component in the plurality of two-dimensional frequency components, performing high-frequency information extraction and synthesis to obtain a super-resolution reconstructed image. For details, please refer to step S204 of the embodiment shown in Figure 2 , which will not be described here again.

[0072] The image scanning microscopic super-resolution reconstruction method based on frequency decomposition provided in this embodiment separates the modal components from high frequency to low frequency in the original dot array image through iterative screening, and finally obtains the modal functions and residual components of different frequencies, which follows the separation logic of high frequency first and low frequency later. The first obtained modal component corresponds to the highest frequency information of the image, the subsequent components correspond to the medium frequency texture and the low frequency background in turn, and the final residual component is the stationary trend base of the image. The layered boundary is clear, which not only ensures the orderliness of frequency decomposition, but also maximizes the preservation of the local detail features of the original dot array image, and provides high-quality and layered clear basic data for subsequent component processing and super-resolution reconstruction.

[0073] The application provides a frequency decomposition-based image scanning microscopic super-resolution reconstruction method, which can be used for the terminal device, Figure 4 A flowchart of a frequency decomposition-based image scanning microscopic super-resolution reconstruction method according to an embodiment of the application is shown in Figure 4 The flowchart includes the following steps: In step S401, a set of original image data blocks is obtained; the set of original image data blocks includes a plurality of original lattice images obtained by two-dimensional scanning of a target sample by using an image scanning microscopic system. For details, refer to step S301 of the embodiment shown in Figure 3 The step S301 of the embodiment shown in is not repeated here.

[0074] In step S402, the original lattice images are subjected to multi-level frequency decomposition, and the highest frequency noise component is filtered out to obtain a plurality of two-dimensional frequency components. For details, refer to step S302 of the embodiment shown in Figure 3 The step S302 of the embodiment shown in is not repeated here.

[0075] In step S403, the plurality of two-dimensional frequency components are subjected to fusion processing to generate a binary digital pinhole mask.

[0076] Specifically, the step S403 includes the following steps: In step S4031, the plurality of two-dimensional frequency components are subjected to component fusion to obtain an intermediate image.

[0077] Specifically, one or more medium-high frequency components (generally to , ) containing main sample structure information but having low noise are selected from the decomposed two-dimensional frequency components, and the two-dimensional frequency components are superimposed and fused to generate an intermediate image with significantly improved signal-to-noise ratio, which retains the morphological characteristics of the light spot but effectively suppresses the background noise.

[0078] In step S4032, a solid circular structure is recognized and extracted by using a solid circular spot automatic detection algorithm based on the intermediate image; the solid circular structure conforms to the diffraction-limited light spot characteristics.

[0079] Specifically, the solid circular spot automatic detection algorithm is applied to the fused intermediate image , which intelligently recognizes the solid circular structure in the intermediate image that conforms to the diffraction-limited light spot characteristics and has a specified diameter of pixels, which is the half-height width of the Gaussian light spot of the imaging system theoretically limited by diffraction.

[0080] Step S4033, the pixel gray information corresponding to the solid circular structure is binarized to obtain a binarized digital pinhole mask.

[0081] Specifically, the pixel gray information in the detected solid circular structure region is binarized using a global image threshold method, and the pixel gray value greater than the threshold is set to 1, otherwise it is set to 0. The threshold is a global threshold, which can be set to 0, thereby generating a corresponding binarized digital pinhole mask for each image block. The binarized digital pinhole mask accurately calibrates the effective spatial distribution of the light spot.

[0082] Step S404, based on the binarized digital pinhole mask and the highest two-dimensional frequency component in the plurality of two-dimensional frequency components, high-frequency information extraction and synthesis are performed to obtain a super-resolution reconstructed image. For details, please refer to Figure 3 Step S304 of the embodiment shown in the figure will not be repeated here.

[0083] The image scanning microscopic super-resolution reconstruction method based on frequency decomposition provided in this embodiment performs component fusion on a plurality of two-dimensional frequency components to obtain an intermediate image, retains the morphological characteristics of the light spot, improves the signal-to-noise ratio, and effectively suppresses the background noise. In addition, the solid circular spot automatic detection algorithm is used to identify and extract the solid circular structure, accurately identify the diffraction-limited light spot, reduce the manual intervention, and improve the processing speed. Finally, the pixel gray information corresponding to the solid circular structure is binarized to convert the solid circular structure into a binarized digital pinhole mask. For light spots under different samples and different imaging conditions, the mask can automatically adjust the number and distribution of pinholes without manual parameter modification. The binarized digital pinhole mask dynamically adapts to the light spot characteristics, supports super-resolution reconstruction, and solves the problems of poor image quality, difficult feature positioning, and poor adaptability of subsequent processing.

[0084] In this embodiment, an image scanning microscopic super-resolution reconstruction method based on frequency decomposition is provided, which can be used in the terminal device described above, Figure 5 is a flowchart of the image scanning microscopic super-resolution reconstruction method based on frequency decomposition according to an embodiment of the present application, as Figure 5 shown, the flowchart includes the following steps: Step S501, obtaining a set of original image data blocks; wherein the set of original image data blocks includes a plurality of original dot array images obtained by two-dimensionally scanning a target sample using an image scanning microscopic system. For details, please refer to Figure 4 Step S401 of the embodiment shown in the figure will not be repeated here.

[0085] Step S502, performing multi-level frequency decomposition on the original dot array image to filter out the highest frequency noise component to obtain a plurality of two-dimensional frequency components. For details, please refer to Figure 4 Step S402 of the embodiment shown in the figure will not be repeated here.

[0086] Step S503, fuse the plurality of two-dimensional frequency components to generate a binary digital pinhole mask. For details, please refer to Figure 4 Step S403 of the embodiment shown, which will not be repeated here.

[0087] Step S504, based on the binary digital pinhole mask and the highest two-dimensional frequency component in the plurality of two-dimensional frequency components, high-frequency information extraction and synthesis are performed to obtain a super-resolution reconstructed image.

[0088] Specifically, the above step S504 includes: Step S5041, multiply the binary digital pinhole mask and the highest two-dimensional frequency component pixel by pixel to obtain a super-resolution image block.

[0089] Specifically, the binary digital pinhole mask and the highest two-dimensional frequency component (i.e. the next component after discarding the highest frequency noise component) are multiplied pixel by pixel, and the binary digital pinhole mask "cuts out" the high-frequency details corresponding to the true signal, while setting the background area to zero, and finally obtains a single super-resolution image block with enhanced resolution and without background noise Wherein, the pixel-by-pixel multiplication can be represented as:

[0090] Step S5042, map the pixels corresponding to the super-resolution image block to the global grid in parallel to generate an initial super-resolution image.

[0091] Specifically, according to the super-resolution image block corresponding to the original scanning coordinates , all its pixels are mapped to a global grid with higher resolution (i.e. smaller pixel size) in parallel, and when the pixels of multiple image blocks are mapped to the same position of the global grid, their intensity values are added up. Finally, by summing the entire grid, a complete initial super-resolution image is generated.

[0092] Step S5043, image post-processing is performed on the initial super-resolution image to obtain a super-resolution reconstructed image.

[0093] Specifically, the initial super-resolution image is subjected to necessary contrast stretching or normalization processing to optimize the visual effect, and the final super-resolution reconstructed image is output.

[0094] The image scanning microscopic super-resolution reconstruction method based on frequency decomposition provided in the embodiment can more fully utilize the photon information collected by the detector by directly extracting and retaining the complete high-frequency component after adaptive mask processing, thereby theoretically having the potential to explore higher limit resolution.

[0095] The specific steps of the image scanning microscopic super-resolution reconstruction method based on frequency decomposition are described below through a specific embodiment.

[0096] Embodiment 1 As shown in Figure 6 , the super-resolution imaging algorithm based on Fourier transform and pixel relocation includes: sequentially performing Fourier transform on m original image data to obtain frequency domain images, performing frequency domain filtering on the frequency domain images, and then converting the processed frequency domain data back to the spatial domain through inverse Fourier transform; performing image opening operation on the inverse transformed images to further remove noise and refine the outline of bright spots, thereby obtaining clearer sampling point images; using the difference in the sampling point positions of each image in the original data, mapping the bright spots of the m images to a unified high-resolution pixel grid; and performing dot array superposition on the m repositioned images to obtain a super-resolution image. As can be seen, the above super-resolution imaging algorithm based on Fourier transform has obvious shortcomings such as frequency domain limitation, strong dependence on data, and limited application scenarios. The algorithm needs to sequentially perform Fourier transform, filtering, and inverse transform on each original low-resolution image, resulting in high computational complexity.

[0097] In addition, the relevant pixel relocation method simply superimposes the wide-field resolution images generated by the original data, and the resolution of the generated wide-field resolution images is low.

[0098] Therefore, the embodiment provides an image scanning microscopic super-resolution reconstruction method based on frequency decomposition, as shown in Figure 7 , the overall process of the image scanning microscopic super-resolution reconstruction method based on frequency decomposition includes: As shown in Figure 8 , in the image reconstruction of image scanning microscopic imaging, the original dot array data is decomposed into several two-dimensional frequency components from high frequency to low frequency, and the highest frequency component corresponds to the high-frequency noise in the image, which is discarded in the subsequent calculation.

[0099] After the fusion processing of some medium and high frequency components in the several two-dimensional frequency components, an intermediate image with significantly improved signal-to-noise ratio is generated.

[0100] Subsequently, using a solid circular spot automatic detection algorithm, the solid circular structure meeting the diffraction limit spot characteristics is intelligently identified and extracted in the fusion image, and then it is converted into an adaptive binary digital pinhole mask.

[0101] The binary digital pinhole mask is applied to the corresponding next highest frequency component, so as to completely suppress the background noise while accurately preserving and enhancing the high-frequency information representing the super-resolution details.

[0102] Finally, all the processed dot array images are superimposed in parallel to obtain a high signal-to-noise ratio and high resolution reconstruction result, i.e. a super-resolution reconstruction image.

[0103] The above embodiment 1 has the following advantages: 1) The complex steps such as peak detection, sub-pixel fitting (such as Gaussian fitting) and fixed parameter digital pinhole setting, which are necessary and highly dependent on manual experience in the pixel reassignment method, are completely avoided, and the entire process is highly automated. Through the standardized process of "frequency decomposition-fusion-mask", the requirement for the professional background of the operator is greatly reduced, the repeatability and ease of use of the method are improved, and the method is conducive to the popularization and standardized application of the technology.

[0104] 2) In the related pixel reassignment method, the digital pinhole will inevitably discard the useful high-frequency information in the periphery of the light spot while suppressing the background. Therefore, the present embodiment directly extracts and retains the complete high-frequency component after adaptive mask processing, more fully utilizes the photon information collected by the detector, and thus has the potential to explore higher limit resolution in theory.

[0105] In summary, through the fundamental change of the technical paradigm, the present embodiment successfully solves the three core pain points of slow processing speed, sensitivity to signal-to-noise ratio and complex process of the pixel reassignment method for a long time, and provides an ultra-resolution imaging tool which realizes a leap-forward improvement in speed, robustness and ease of use, and effectively promotes the in-depth application of image scanning microscopy in the fields of life science research and industrial detection.

[0106] In the present embodiment, an image scanning microscopy super-resolution reconstruction device based on frequency decomposition is also provided, which is used to implement the above embodiments and preferred embodiments, and will not be described again. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware or a combination of software and hardware is also possible and contemplated.

[0107] The present embodiment provides an image scanning microscopy super-resolution reconstruction device based on frequency decomposition, as shown in Figure 9 includes: The acquisition module 901 is configured to acquire an original image data block set, wherein the original image data block set includes a plurality of original dot array images obtained by two-dimensional scanning of a target sample by an image scanning microscopy system; The multi-level frequency decomposition module 902 is configured to perform multi-level frequency decomposition on the original dot array image to filter out the highest frequency noise component, and obtain a plurality of two-dimensional frequency components; The fusion processing module 903 is configured to perform fusion processing on the plurality of two-dimensional frequency components to generate a binary digital pinhole mask; The extraction and synthesis module 904 is configured to perform high-frequency information extraction and synthesis based on the binarized digital pinhole mask and the highest two-dimensional frequency component in the plurality of two-dimensional frequency components, to obtain a super-resolution reconstructed image.

[0108] In some optional embodiments, the multi-level frequency decomposition module 902 comprises: A screening unit configured to perform frequency screening on the original lattice image to obtain a candidate modal function; A calculation unit configured to subtract the candidate modal function from the original lattice image to obtain a residual component; An iterative decomposition unit configured to perform iterative decomposition on the residual component to obtain a plurality of modal functions; A sorting unit configured to sort the plurality of modal functions in descending order of frequency to obtain a plurality of modal components; A filtering unit configured to extract a highest-frequency noise component from the plurality of modal components and filter out the highest-frequency noise component to obtain the plurality of two-dimensional frequency components.

[0109] In some optional embodiments, the screening unit comprises: An initialization subunit configured to initialize the original lattice image to obtain a local extreme point; An interpolation subunit configured to perform surface interpolation on the local extreme point to obtain an upper envelope surface and a lower envelope surface, and calculate a mean envelope surface based on the upper envelope surface and the lower envelope surface; A calculation subunit configured to calculate a difference between the original lattice image and the mean envelope surface to obtain the candidate modal function; A comparison subunit configured to compare the candidate modal function with a modal function condition, and output the candidate modal function if the candidate modal function satisfies the modal function condition.

[0110] In some optional embodiments, the filtering unit comprises: A determination subunit configured to calculate a structural similarity index between the plurality of modal components, and determine the highest-frequency noise component based on the structural similarity index; A separation and filtering subunit configured to separate and filter out the highest-frequency noise component from the plurality of modal components to obtain the plurality of two-dimensional frequency components.

[0111] In some optional embodiments, the fusion processing module 903 comprises: A fusion unit configured to perform component fusion on the plurality of two-dimensional frequency components to obtain an intermediate image; The extraction unit is configured to identify and extract a solid circular structure from the intermediate image based on the intermediate image by using a solid circular spot automatic detection algorithm, wherein the solid circular structure meets a diffraction limit spot feature. The binarization processing unit is configured to perform binarization processing on pixel gray scale information corresponding to the solid circular structure to obtain the binarized digital pinhole mask.

[0112] In some optional embodiments, the extraction and synthesis module 904 includes: The multiplication unit is configured to perform pixel-by-pixel multiplication between the binarized digital pinhole mask and the highest two-dimensional frequency component to obtain a super-resolution image block. The mapping unit is configured to perform parallel mapping of pixels corresponding to the super-resolution image block to a global grid to generate an initial super-resolution image. The image post-processing unit is configured to perform image post-processing on the initial super-resolution image to obtain the super-resolution reconstructed image.

[0113] The image scanning microscopic super-resolution reconstruction device based on frequency decomposition provided by the embodiments of the present application can perform the image scanning microscopic super-resolution reconstruction method based on frequency decomposition provided by any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of performing the method. The further function description of each of the above modules and units is the same as that of the corresponding embodiments described above, and will not be repeated here.

[0114] Figure 10 A structural schematic diagram of an electronic device provided by the embodiments of the present application is provided.

[0115] Reference will be made in detail to Figure 10 which shows a structural schematic diagram of an electronic device suitable for implementing the electronic device in the embodiments of the present application. The electronic device can include a processor (such as a central processor, a graphics processor, etc.) 1001, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage 1008 to a random access memory (RAM) 1003. In the RAM 1003, various programs and data required for operation of the electronic device are also stored. The processor 1001, the ROM 1002, and the RAM 1003 are connected to each other through a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0116] Generally, the following devices can be connected to the I / O interface 1005: input devices 1006, including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, and the like; output devices 1007, including, for example, a liquid crystal display (LCD), a speaker, a vibrator, and the like; storage devices 1008, including, for example, a magnetic tape, a hard disk, and the like; and communication devices 1009. The communication devices 1009 can allow the electronic device to communicate wirelessly or via a wire with other devices to exchange data. Although Figure 10 An electronic device having various devices is shown, but it is understood that all of the shown devices are not required and more or fewer devices can alternatively be implemented.

[0117] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program according to embodiments of the present application. For example, embodiments of the present application include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication devices 1009, or installed from the storage devices 1008, or installed from the ROM 1002. When the computer program is executed by the processor 1001, the above-described functions defined in a frequency decomposition based image scanning microscopic super-resolution reconstruction method according to embodiments of the present application are performed.

[0118] Figure 10 The electronic device shown is merely an example and should not impose any limitation on the functions and the scope of use of embodiments of the present application.

[0119] Embodiments of the present application also provide a computer-readable storage medium, the above-mentioned method according to embodiments of the present application can be implemented in hardware, firmware, or as computer code that can be recorded on a storage medium, or stored in a remote storage medium or a non-transitory machine-readable storage medium and stored in a local storage medium to be downloaded through a network, so that the method described herein can be processed by such software stored on a storage medium using a general-purpose computer, a special-purpose processor, or programmable or special-purpose hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk or a solid state disk, etc.; further, the storage medium can also include a combination of the above-mentioned types of storage. It can be understood that the computer, processor, microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code, which, when accessed and executed by the computer, processor or hardware, implements the frequency decomposition based image scanning microscopic super-resolution reconstruction method shown in the above embodiments.

[0120] Part of the present application can be applied as a computer program product, for example, computer program instructions, when executed by a computer, through the operation of the computer, can invoke or provide the method and / or technical solutions according to the present application. Those skilled in the art should understand that the form of computer program instructions in computer readable medium includes but is not limited to source files, executable files, installation package files and the like, and accordingly, the way of computer program instructions executed by computer includes but is not limited to: the computer directly executes the instructions, or the computer compiles the instructions and then executes the corresponding compiled program, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Here, the computer readable medium can be any available computer readable storage medium or communication medium accessible to the computer.

[0121] Although the embodiments of the present application are described in conjunction with the drawings, various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes fall within the scope defined by the appended claims.

Claims

1. A frequency-decomposition-based image scanning microscopic super-resolution reconstruction method, characterized in that, The method comprises: obtaining a set of original image data blocks; wherein the set of original image data blocks comprises a plurality of original dot array images obtained by two-dimensional scanning of a target sample by an image scanning microscopy system; performing multi-level frequency decomposition on the original dot array images, filtering out the highest frequency noise component, to obtain a plurality of two-dimensional frequency components; performing fusion processing on the plurality of two-dimensional frequency components to generate a binary digital pinhole mask; based on the binary digital pinhole mask and the highest two-dimensional frequency component of the plurality of two-dimensional frequency components, performing high-frequency information extraction and synthesis to obtain a super-resolution reconstructed image.

2. The method of claim 1, wherein, The method comprises: performing multi-level frequency decomposition on the original dot array images, filtering out the highest frequency noise component, to obtain a plurality of two-dimensional frequency components, comprising: performing frequency screening on the original dot array images to obtain a candidate modal function; subtracting the candidate modal function from the original dot array image to obtain a residual component; performing iterative decomposition on the residual component to obtain a plurality of modal functions; sorting the plurality of modal functions from high to low in frequency to obtain a plurality of modal components; 3. The method of claim 2, wherein, extracting the highest frequency noise component from the plurality of modal components and filtering out the highest frequency noise component to obtain a plurality of two-dimensional frequency components. The method comprises: performing frequency screening on the original dot array images to obtain a candidate modal function, comprising: initializing the original dot array image to obtain local extreme points; performing surface interpolation on the local extreme points to obtain an upper envelope surface and a lower envelope surface, and calculating a mean envelope surface based on the upper envelope surface and the lower envelope surface; 4. The method of claim 2, wherein, calculating the difference between the original dot array image and the mean envelope surface to obtain the candidate modal function; comparing the candidate modal function with a modal function condition, and if the candidate modal function satisfies the modal function condition, outputting the candidate modal function. The method comprises:

5. The method of claim 1, wherein, extracting the highest frequency noise component from the plurality of modal components and filtering out the highest frequency noise component to obtain a plurality of two-dimensional frequency components, comprising: calculating the structural similarity index between the plurality of modal components, and determining the highest frequency noise component based on the structural similarity index; separating and filtering out the highest frequency noise component from the plurality of modal components to obtain a plurality of two-dimensional frequency components. The method comprises:

6. The method of claim 1, wherein, performing fusion processing on the plurality of two-dimensional frequency components to generate a binary digital pinhole mask, comprising: performing component fusion on the plurality of two-dimensional frequency components to obtain an intermediate image; based on the intermediate image, using a solid circular spot automatic detection algorithm to identify and extract a solid circular structure; wherein the solid circular structure conforms to the diffraction limit spot feature; performing binary processing on the pixel gray scale information corresponding to the solid circular structure to obtain the binary digital pinhole mask. The method comprises: based on the binary digital pinhole mask and the highest two-dimensional frequency component of the plurality of two-dimensional frequency components, performing high-frequency information extraction and synthesis to obtain a super-resolution reconstructed image, comprising: performing pixel-by-pixel multiplication on the binary digital pinhole mask and the highest two-dimensional frequency component to obtain a super-resolution image block; Mapping the pixels corresponding to the super-resolution image block into a global grid in parallel to generate an initial super-resolution image; Performing image post-processing on the initial super-resolution image to obtain the super-resolution reconstruction image.

7. An apparatus for frequency-decomposition-based image scanning microscopic super-resolution reconstruction, characterized in that, The device comprises: An acquisition module configured to acquire a set of original image data blocks, wherein the set of original image data blocks comprises a plurality of original lattice images obtained by two-dimensional scanning of a target sample using an image scanning microscopy system; A multi-level frequency decomposition module configured to perform multi-level frequency decomposition on the original lattice images to filter out the highest frequency noise components and obtain a plurality of two-dimensional frequency components; A fusion processing module configured to perform fusion processing on the plurality of two-dimensional frequency components to generate a binary digital pinhole mask; An extraction and synthesis module configured to perform high-frequency information extraction and synthesis based on the binary digital pinhole mask and the highest two-dimensional frequency component of the plurality of two-dimensional frequency components to obtain a super-resolution reconstruction image.

8. An electronic device, comprising: It comprises: A memory and a processor, which are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the frequency decomposition-based image scanning microscopy super-resolution reconstruction method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for causing a computer to execute the frequency decomposition-based image scanning microscopy super-resolution reconstruction method according to any one of claims 1 to 6.

10. A computer program product, characterised in that, It comprises computer instructions for causing a computer to execute the frequency decomposition-based image scanning microscopy super-resolution reconstruction method according to any one of claims 1 to 6.

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