Image imaging parameter indexing method, system, apparatus, and computer program product

Abstract

The application provides an image imaging parameter indexing method, system, device and computer program product, and the method comprises the following steps: generating a simulation data set according to a preset first image and a preset first label; comparing the simulation data set with a first target image to obtain a second label corresponding to the first image in one-to-one manner; iteratively setting the first label according to the first label and the second label to obtain a label database composed of N second labels; analyzing the label database to obtain at least one second label as a first optimal label and obtain a label value of the first optimal label; the imaging parameter comparison between a simulation image and an experimental image is realized, and the image imaging parameter indexing is completed, and errors caused by manual comparison are avoided.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and in particular to methods, systems, devices, and computer program products for indexing image imaging parameters. Background Technology

[0002] In related technologies, by using a spherical aberration corrector to adjust the spherical aberration coefficient to a negative value and combining it with overfocus imaging condition optimization, it is possible to obtain high-quality negative spherical aberration transmission electron microscopy images without sacrificing atomic-level resolution. This significantly improves the contrast of ultralight elements (such as oxygen) in high-resolution images and provides a way to obtain microstructural information of ultralight elements.

[0003] However, a certain degree of image distortion occurs during the acquisition of negative spherical aberration electron microscopy (EM) images. The extent of image distortion is directly affected by the imaging parameters. Therefore, controlling the imaging parameters of negative spherical aberration images is of great significance for acquiring high-quality negative spherical aberration images. The simulated images of negative spherical aberration EM images are usually subject to the combined regulation and influence of multiple parameters, including sample structure parameters, EM instrument parameters, EM aberration correction degree, and detector parameters.

[0004] Currently, the indexing of imaging parameters for negative spherical aberration images is usually based on repeated comparisons of simulated and experimental images with a series of controlled imaging parameters. This is time-consuming and labor-intensive, and there is a certain degree of error in manual comparison. Therefore, there is an urgent need for a method specifically for evaluating the quality of negative spherical aberration electron microscopy images for the indexing of imaging parameters for negative spherical aberration electron microscopy images. Summary of the Invention

[0005] The embodiments of the present invention provide an image imaging parameter indexing method, system, device, and computer program product to solve the above-mentioned technical problems.

[0006] To achieve the above objectives, in a first aspect, this application provides a method for indexing image imaging parameters, comprising the following steps:

[0007] A simulated dataset is generated based on a preset first image and a preset first label; wherein, the simulated dataset contains N first images and N first labels that correspond one-to-one, the first label being crystallographic information used to characterize the parameter values ​​of manually set image imaging parameters, and N being an integer greater than 1;

[0008] The simulated dataset is compared with the first target image to obtain a second label that corresponds one-to-one with the first image. The second label is used to characterize the image structure similarity index between the first target image and the first image.

[0009] According to the first label and the second label, the first label is iteratively set to obtain a label database composed of N second labels;

[0010] The label database is analyzed to obtain at least one second label as a first optimal label and obtain a label value of the first optimal label.

[0011] Further, the first label includes:

[0012] At least two crystallographic information in atomic coordinates, cell parameters, crystal band axis orientation, Debye-Waller factor, sample thickness, electron gun accelerating voltage, aperture angle, wave function sampling rate, camera modulation contrast function, convergence half angle, sample stage mechanical vibration degree, tilt angle, defocus diffusion, defocus amount, second order astigmatism coefficient, third order astigmatism coefficient, coma coefficient and spherical aberration coefficient.

[0013] Further, the step of obtaining a label database composed of N second labels according to the first label and the second label includes:

[0014] According to the element composition order in the first label, an element sequence of the first label is obtained;

[0015] According to the preset iterative setting method and the element sequence, the value range of each element in the element sequence of the first label is set in sequence, the Cartesian product between all elements is obtained, and the Cartesian product is taken as an element sequence set of the first label;

[0016] According to the second label and the element sequence set, a second label set corresponding to the element sequence set is generated.

[0017] Further, the step of comparing the simulated data set with the first target image to obtain a second label corresponding one-to-one to the first image includes:

[0018] The first image in the simulated data set is sorted to obtain an image sequence;

[0019] According to the image sequence, the first image is compared with the first target image in sequence based on a preset iterative setting method to obtain an SSIM structural similarity index of each first image, wherein the order of the SSIM structural similarity index constitutes an index sequence, and the index sequence corresponds one-to-one to the image sequence.

[0020] The SSIM structural similarity index is taken as the second label and a similarity result data set is composed.

[0021] Further, the iterative setting method comprises the following steps:

[0022] N first labels are set, wherein N is an integer greater than 1;

[0023] N corresponding second labels are obtained;

[0024] A first label corresponding to a second optimal label among the N second labels is obtained, wherein the second optimal label is configured as the second label with the highest value;

[0025] X first labels are finely set based on the first label corresponding to the second optimal label, and X corresponding second label values are obtained, wherein X is an integer greater than 1;

[0026] A first label corresponding to a second optimal label among N+X second labels is obtained.

[0027] Further, before the step of generating a simulation data set comprising N first images according to a preset first image and a preset first label, the method further comprises:

[0028] An original image is obtained and preprocessed to obtain the first target image;

[0029] N first labels are obtained, wherein the first labels are set based on initial labeling of the first target image by a person, and N is an integer greater than 1;

[0030] N first images are obtained according to the N first labels.

[0031] Further, the step of iteratively setting the first labels according to the first labels and the second labels to obtain a label database comprising N second labels comprises:

[0032] The similarity result data set is analyzed to obtain an analysis result data set, wherein the analysis result data set comprises a second label with an optimal SSIM structural similarity index and corresponding parameter values;

[0033] The parameter value interval is optimized to improve the SSIM structural similarity index;

[0034] When the SSIM structural similarity index is iterated to a target value, crystallographic feature adjustment is performed to obtain the first label after iteration and the corresponding second label.

[0035] In a second aspect, the present application provides an image imaging parameter index system, comprising:

[0036] An analog image dataset generation module is configured to generate an analog dataset according to a preset first image and a preset first label, wherein the analog dataset comprises N first images and N first labels, the first image corresponds to one first label, the first label is crystallographic information used to represent a parameter value of an artificially set image imaging parameter, N is an integer greater than 1;

[0037] An image structure similarity comparison module is configured to compare the analog dataset with a first target image to obtain a second label corresponding to the first image, wherein the second label is used to represent an image structure similarity index between the first target image and the first image.

[0038] A loop iteration module is configured to set the first label iteratively according to the first label and the second label to obtain a label database composed of N second labels.

[0039] A data result analysis and output module is configured to analyze the label database to obtain at least one second label as a first optimal label and obtain a label value of the first optimal label.

[0040] In a third aspect, the present application provides an image imaging parameter index device, comprising a memory and a processor, the memory stores at least one program, the at least one program is executed by the processor to realize the method of any one of the first aspect.

[0041] In a fourth aspect, the present application provides a computer program product, comprising a computer program, the computer program is executed by the processor to realize the steps of the method of any one of the first aspect.

[0042] The above technical solution has the following technical effects:

[0043] The present application quickly generates an analog dataset with multiple parameter series (i.e. first label) changes by setting multiple parameter ranges of the preset first image and the first label, compares and judges the analog dataset with the experimental image with high precision to obtain a quantitative value (i.e. second label) of the fitting degree of the analog image relative to the experimental image, iteratively runs the imaging parameters to realize accurate searching and screening of the imaging parameters, integrates and analyzes the obtained data value to obtain a visual result, realizes automatic comparison of the imaging parameters of the analog image and the experimental image and completes the image imaging parameter index, and avoids errors caused by manual comparison, thereby reducing the distortion degree of image distortion in the process of obtaining a negative spherical aberration electron microscope image. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 Fig. 1 is a flowchart of an embodiment of the method for indexing image imaging parameters according to the present application;

[0045] Figure 2 Fig. 2 is a flowchart of another embodiment of the method for indexing image imaging parameters according to the present application;

[0046] Figure 3 Fig. 3 is a schematic diagram of the method for indexing image imaging parameters according to the present application;

[0047] Figure 4 Fig. 4 is a schematic diagram of the system for indexing image imaging parameters according to the present application. DETAILED DESCRIPTION

[0048] To further illustrate the embodiments, the present application provides accompanying drawings. These drawings are part of the disclosure of the present application, which mainly serve to illustrate the embodiments, and can be used to explain the operating principles of the embodiments in conjunction with the related descriptions in the specification. Those of ordinary skill in the art should be able to understand other possible implementations and advantages of the present application by referring to these contents. The components in the drawings are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0049] The present application will be further described in conjunction with the accompanying drawings and specific embodiments.

[0050] It should be noted that indexing diffraction data processing is one of the links. Usually, it is the first step, which determines the unit cell parameters of the crystal and the position orientation of the crystal during data collection by analyzing the positions of diffraction points, and uses this information to correspond each diffraction point to a lattice point in reciprocal space, thereby preparing for subsequent collection of the intensity of each reciprocal lattice point.

[0051] Embodiment 1:

[0052] An embodiment of the method for indexing image imaging parameters includes the following steps:

[0053] S1, generating a simulation data set according to a preset first image and a preset first label; wherein the simulation data set contains one-to-one N first images and N first labels, the first label is crystallographic information, used to represent the parameter value of the artificially set image imaging parameter, and N is an integer greater than 1;

[0054] Optionally, the first label includes:

[0055] at least two crystallographic information from the group consisting of atomic coordinates, cell parameters, orientation of crystallographic axes, Debye-Waller factors, sample thickness, electron gun acceleration voltage, aperture angle, wave function sampling rate, modulation transfer function of the camera, convergence half angle, degree of mechanical vibration of the sample stage, tilt angle, defocus spread, defocus amount, second order astigmatism coefficient, third order astigmatism coefficient, coma coefficient, and spherical aberration coefficient.

[0056] Optionally, before the step of generating the simulation data set containing N first images according to the preset first image and the preset first label, the method further comprises:

[0057] obtaining a raw image and pre-processing the raw image to obtain the first target image;

[0058] obtaining N first labels, wherein the first labels are obtained based on manual initial labeling of the first target image, and N is an integer greater than 1;

[0059] obtaining N first images according to the N first labels;

[0060] Specifically, the raw image can be a negative spherical aberration transmission electron microscope experimental image image1, a region of interest ROI1 of a specific size is selected according to the experimental image image1, and as many regions as possible such as ROI2, ROI3, ROI4, ROI5, etc. with the same structure and consistent with the size of ROI1 are selected according to the periodicity of the unit cell in the experimental image. After image superposition and noise reduction, a first target image image2 is obtained. Crystallographic information such as interplanar spacing in the first target image image2 is measured. According to the obtained crystallographic information, the axial index is determined, and a cif crystallographic information file with indexed structure parameters (unit cell parameters, axial index, Debye-Waller factor) is output, i.e. a first label. The first label is one-to-one corresponding to the first target image image2, and the first image can be obtained.

[0061] When generating the simulation data set, according to the known electron microscope instrument parameter values (voltage, aperture size, wave function sampling rate, detector modulation transfer function, convergence half angle, etc.) in the experimental process, the initial interval range1 of the image simulation parameter values is set. Cartesian product is used for multiple parameters to obtain a parameter array and select a cif crystallographic information file for image simulation to obtain a simulation data set data1 of the first image.

[0062] S2, comparing the simulation data set with the first target image to obtain a second label corresponding to the first image one by one, wherein the second label is used to represent the image structure similarity index between the first target image and the first image;

[0063] Optionally, the step of comparing the simulation data set with the first target image to obtain the second label corresponding to the first image one by one comprises:

[0064] sequencing the first images in the simulation data set to obtain an image sequence;

[0065] comparing the first images with the first target image in sequence based on a preset iterative setting method according to the image sequence to obtain the SSIM structural similarity index of each first image, wherein the sequencing of the SSIM structural similarity index constitutes an index sequence, and the index sequence corresponds to the image sequence one by one;

[0066] using the SSIM structural similarity index as the second label;

[0067] Further optionally, the iterative setting method comprises the following steps:

[0068] setting N first labels, wherein N is an integer greater than 1;

[0069] obtaining corresponding N second labels;

[0070] obtaining the first label corresponding to the second optimal label among the N second labels, wherein the second optimal label is configured as the second label with the highest value;

[0071] based on the first label corresponding to the second optimal label, setting X first labels in detail and obtaining corresponding X second label values, wherein X is an integer greater than 1;

[0072] obtaining the first label corresponding to the second optimal label among the N+X second labels.

[0073] Specifically, the simulation data set data1 of the first image is compared with the first target image image2 in terms of the SSIM structural similarity index to obtain the similarity result data set data2 corresponding to the images in the simulation data set data1.

[0074] S3, according to the first label and the second label, iteratively setting the first label to obtain a label database composed of N second labels;

[0075] Optionally, the step of iteratively setting the first label according to the first label and the second label to obtain a label database composed of N second labels comprises:

[0076] The analysis result data set is obtained by analyzing the similarity result data set data 2, wherein the analysis result data set comprises a second label with an optimal SSIM structural similarity index and corresponding parameter values;

[0077] The parameter value interval is optimized to improve the SSIM structural similarity index;

[0078] When the SSIM structural similarity index is iterated to a target value, crystallographic feature adjustment is performed to obtain the first label after iteration and the corresponding second label.

[0079] S4, analyzing the label database to obtain at least one second label as a first optimal label and obtain the label value of the first optimal label;

[0080] Specifically, the SSIM structural similarity index with at least one highest parameter value and the corresponding parameter value, i.e., the first optimal label, are outputted, and the image imaging parameter index of the first target image is completed;

[0081] Optionally, the completed simulation data set can also be written into the database. As the user continuously enriches the database content by generating simulation data sets under different parameters, when other users use the same parameters, the simulation data set in the database can be directly read for index comparison and analysis, reducing the repeated and redundant time of simulation data set and accelerating the data processing progress.

[0082] Embodiment two:

[0083] Referring to Figure 2 An embodiment of an image imaging parameter index method is applied to the quantitative analysis of DyScO3 atomic-level negative spherical aberration image imaging parameters, comprising the following steps:

[0084] S21, obtaining an original image and pre-processing the original image to obtain the first target image;

[0085] Specifically, referring to Figure 3 The DyScO3 atomic-level negative spherical aberration image is inputted as an original image, a specific size of a region of interest ROI1 is selected, and as many regions as possible such as ROI2, ROI3, ROI4, and ROI5 with the same structure and consistent size as ROI1 are selected in the experimental image according to the periodicity of the DyScO3 unit cell. After image superposition and noise reduction, the first target image is obtained.

[0086] S22, obtaining N first labels, wherein the first labels are obtained based on manual initial calibration and setting of the first target image, and N is an integer greater than 1;

[0087] Specifically, the first target image is measured for crystallographic information such as interplanar spacing, atomic coordinates, cell parameters, and orientation of zone axis, indexation is performed on the zone axis based on the obtained crystallographic information, and a cif crystallographic information file with indexed structure parameters (cell parameters, zone axis, and Debye-Waller factor) is outputted, so as to obtain the first label, and the number N of the first label is the same as the number of crystallographic information.

[0088] S23, obtaining N first images according to N first labels.

[0089] Specifically, the first label is one-to-one corresponding to the first target image, so that N first images can be obtained.

[0090] S24, generating a simulation data set according to a preset first image and a preset first label; wherein the simulation data set comprises N first images and N first labels corresponding one-to-one, the first label is crystallographic information, and is used to represent a parameter value of an image imaging parameter set artificially, and N is an integer greater than 1.

[0091] Specifically, in the process of generating the simulation data set, the initial interval range1 of the image simulation parameter value is set according to the known electron microscope instrument parameter value (voltage, aperture size, sampling rate of wave function, modulation transfer function of detector, convergence half angle, etc.) in the experiment process, the Cartesian product is used for multiple parameters, the parameter array is obtained, and the cif crystallographic information file is selected for image simulation, so as to obtain the simulation data set data1 of the first image.

[0092] S25, comparing the simulation data set with the first target image to obtain a second label corresponding one-to-one to the first image, wherein the second label is used to represent an image structure similarity index between the first target image and the first image.

[0093] Specifically, a preset iteration setting method is obtained, including:

[0094] Setting N first labels, wherein N is an integer greater than 1.

[0095] Obtaining corresponding N second labels.

[0096] Obtaining a first label corresponding to a second optimal label in N second labels, wherein the second optimal label is configured as the second label with the highest value.

[0097] Based on the first label corresponding to the second optimal label, X first labels are finely set and corresponding X second label values are obtained, wherein X is an integer greater than 1.

[0098] Obtaining a first label corresponding to a second optimal label in N+X second labels.

[0099] Based on the above iterative setting method, the first image is compared with the first target image in turn, and the SSIM structural similarity index of each first image is obtained, wherein the order of the SSIM structural similarity index constitutes an index sequence, and the index sequence corresponds to the image sequence one by one.

[0100] The SSIM structural similarity index is taken as the second label.

[0101] S26, analyze the similarity result data set to obtain an analysis result data set, wherein the analysis result data set includes a second label with the optimal SSIM structural similarity index and corresponding parameter values.

[0102] S27, optimize the parameter value interval to improve the SSIM structural similarity index.

[0103] Specifically, steps S24 to S26 are repeated to optimize the parameter value interval and improve the SSIM structural similarity index.

[0104] S28, when the SSIM structural similarity index is iterated to a target value, the crystallographic characteristics are adjusted to obtain the first label after iteration and the corresponding second label.

[0105] Specifically, in this embodiment, when the continuous iteration reaches a specific index (0.9) requirement, the atomic polarization, doping, vacancy and Debye-Waller factor of crystallographic characteristics are adjusted, the cif file is output, and steps S24 to S27 are repeated.

[0106] S29, analyze the label database to obtain at least one second label as a first optimal label and obtain the label value of the first optimal label.

[0107] Specifically, the parameter value corresponding to at least one optimal SSIM structural similarity index is output, and the image imaging parameter index of the first target image is completed.

[0108] Embodiment three:

[0109] The application also provides an image imaging parameter index system, referring to Figure 4 , comprising:

[0110] The simulation image data set generation module is configured to generate a simulation data set according to a preset first image and a preset first label; wherein the simulation data set contains one-to-one N first images and N first labels, the first label is crystallographic information, used to represent the parameter value of the artificially set image imaging parameter, and N is an integer greater than 1.

[0111] an image structure similarity comparison module, configured to compare the simulation data set with a first target image, and obtain a second label corresponding to the first image, wherein the second label is used to represent an image structure similarity index between the first target image and the first image;

[0112] a loop iteration module, configured to set the first label iteratively according to the first label and the second label, and obtain a label database composed of N second labels;

[0113] a data result analysis and output module, configured to analyze the label database, obtain at least one second label as a first optimal label, and obtain a label value of the first optimal label.

[0114] Embodiment Four:

[0115] The application further provides an image imaging parameter index device, comprising a memory and a processor, wherein the memory stores at least one program, and the at least one program is executed by the processor to realize the method as described above.

[0116] Further, as an executable scheme, the image imaging parameter index device can be a computer unit, which can be a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The computer unit can comprise, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above-mentioned component structure of the computer unit is only an example of the computer unit, and does not constitute a limitation on the computer unit, and can comprise more or fewer components than the above, or combine certain components, or different components. For example, the computer unit can also comprise an input / output device, a network access device, a bus and the like, and the embodiments of the application do not limit this.

[0117] Further, as an executable solution, the processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor or the like, which is a control center of the computer unit, and connects all parts of the computer unit through various interfaces and lines.

[0118] The memory can be used to store the computer program and / or modules, and the processor realizes various functions of the computer unit by running or executing the computer program and / or modules stored in the memory, and calling data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system and at least one application required by a function; and the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a nonvolatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory device.

[0119] Embodiment five:

[0120] The application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the method of the above-mentioned embodiments of the application.

[0121] Although the application is specifically shown and described in connection with the preferred embodiments, those skilled in the art should understand that various changes in form and details can be made to the application without departing from the spirit and scope of the application as defined in the appended claims.

Claims

1. A method for indexing image imaging parameters, characterized in that, Includes the following steps: A simulated dataset is generated based on a preset first image and a preset first label; wherein, the simulated dataset contains N first images and N first labels that correspond one-to-one, the first label being crystallographic information used to characterize the parameter values ​​of manually set image imaging parameters, and N being an integer greater than 1; The simulated dataset is compared with the first target image to obtain a second label that corresponds one-to-one with the first image. The second label is used to characterize the image structure similarity index between the first target image and the first image. Based on the first tag and the second tag, the first tag is iteratively set to obtain a tag database consisting of N second tags; The tag database is analyzed to obtain at least one second tag as the first optimal tag and the tag value of the first optimal tag is obtained; The process of comparing the simulated dataset with the first target image to obtain a second label corresponding to each of the first images includes: Sort the first image in the simulated dataset to obtain an image sequence; According to the image sequence, based on a preset iterative setting method, the first image is compared with the first target image in turn to obtain the SSIM structural similarity index of each first image. The order of the SSIM structural similarity indexes constitutes an index sequence, and the index sequence corresponds one-to-one with the image sequence. The SSIM structural similarity index is used as the second label to form a similarity result dataset; Specifically, based on the first tag and the second tag, the first tag is iteratively set to obtain a tag database consisting of N second tags, including: The similarity result dataset is analyzed to obtain an analysis result dataset, wherein the analysis result dataset includes the second label with the best SSIM structural similarity index and the corresponding parameter values; The parameter value range is optimized to improve the SSIM structural similarity index; When the SSIM structure similarity index is iterated to the target value, crystallographic feature adjustment is performed to obtain the iterated first label and the corresponding second label. At least one second label with the best SSIM structure similarity index is output as the first optimal label and the parameter values ​​corresponding to the first optimal label.

2. The image imaging parameter indexing method according to claim 1, characterized in that, The first tag includes: At least two of the following crystallographic information are required: atomic coordinates, cell parameters, zone axis orientation, Debye-Waller factor, sample thickness, electron gun accelerating voltage, aperture angle, wavefunction sampling rate, camera modulation contrast function, convergence half angle, sample stage mechanical vibration degree, tilt angle, defocus diffusion, defocus amount, second-order astigmatism coefficient, third-order astigmatism coefficient, coma coefficient, and spherical aberration coefficient.

3. The image imaging parameter indexing method according to claim 2, characterized in that, The step of iteratively setting the first tag based on the first tag and the second tag to obtain a tag database consisting of N second tags includes: Based on the element composition in the first tag, the element sequence of the first tag is obtained by sorting. According to the preset iterative setting method and the element sequence, the value range of each element in the element sequence of the first label is set in sequence to obtain the Cartesian product between all elements and the Cartesian product is used as the element sequence set of the first label. Based on the second tag and the feature sequence set, a second tag set corresponding to the feature sequence set is generated; wherein, the second tag set constitutes the tag database.

4. The image imaging parameter indexing method according to claim 1, characterized in that, The iterative setting method includes the following steps: Define N first tags, where N is an integer greater than 1; Obtain the corresponding N second tags; Obtain the first tag corresponding to the second optimal tag among N second tags, wherein the second optimal tag is configured as the second tag with the highest value; Based on the first label corresponding to the second optimal label, X first labels are refined and X corresponding second label values ​​are obtained, where X is an integer greater than 1; Get the first label corresponding to the second best label among N+X second labels.

5. The image imaging parameter indexing method according to claim 1, characterized in that, Before the step of generating a simulated dataset containing N first images based on preset first images and preset first labels, the method further includes: The original image is acquired and preprocessed to obtain the first target image; Obtain N first labels, wherein the first labels are obtained based on the initial calibration setting of the first target image by manual means, and N is an integer greater than 1; Based on N of the first labels, obtain N of the first images.

6. An image imaging parameter indexing system, characterized in that, include: A simulated image dataset generation module is used to generate a simulated dataset based on a preset first image and a preset first label; wherein, the simulated dataset contains N first images and N first labels that correspond one-to-one, the first label being crystallographic information used to characterize the parameter values ​​of manually set image imaging parameters, and N being an integer greater than 1; An image structure similarity comparison module is used to compare the simulated dataset with a first target image to obtain a second label that corresponds one-to-one with the first image. The second label is used to characterize the image structure similarity index between the first target image and the first image. The loop iteration module is used to iteratively set the first tag based on the first tag and the second tag to obtain a tag database composed of N second tags; The data result analysis and output module is used to analyze the tag database, obtain at least one second tag as the first optimal tag, and obtain the tag value of the first optimal tag. The process of comparing the simulated dataset with the first target image to obtain a second label corresponding to each of the first images includes: Sort the first image in the simulated dataset to obtain an image sequence; According to the image sequence, based on a preset iterative setting method, the first image is compared with the first target image in sequence to obtain the SSIM structural similarity index of each first image. The order of the SSIM structural similarity index constitutes an index sequence, and the index sequence corresponds one-to-one with the image sequence. The SSIM structural similarity index is used as the second label to form a similarity result dataset; Specifically, based on the first tag and the second tag, the first tag is iteratively set to obtain a tag database consisting of N second tags, including: The similarity result dataset is analyzed to obtain an analysis result dataset, wherein the analysis result dataset includes the second label with the best SSIM structural similarity index and the corresponding parameter values; The parameter value range is optimized to improve the SSIM structural similarity index; When the SSIM structural similarity index is iterated to the target value, crystallographic feature adjustment is performed to obtain the iterated first label and the corresponding second label; The data result analysis and output module is used to output at least one second label with the best SSIM structural similarity index as the first optimal label and the parameter values ​​corresponding to the first optimal label.

7. An image imaging parameter indexing device, characterized in that, It includes a memory and a processor, the memory storing at least one program, the at least one program being executed by the processor to implement the method as claimed in any one of claims 1 to 5.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5.

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