Scanning Electron Microscope Image Stitching Method, Device, Electronic Device and Storage Medium

Abstract

The present application relates to the field of image processing, and discloses a scanning electron microscope image stitching method, apparatus, electronic device, and computer-readable storage medium. Among them, the scanning electron microscope image stitching method includes: for each shooting point in the scanning electron microscope, obtaining a shooting point image group captured at the shooting point, the shooting point image group including a plurality of shooting point images with different imaging principles; for any adjacent shooting points, determining a pair of target shooting point images with the highest similarity from the shooting point image groups corresponding to the adjacent shooting points; and performing image stitching on the pair of target shooting point images. Compared with the related art, the scanning electron microscope image stitching method, apparatus, electronic device, and computer-readable storage medium provided by the embodiments of the present application have the advantage of being able to improve the stitching effect when the scanning electron microscope performs image stitching.

Description

Technical Field

[0001] This application relates to the field of image processing. Specifically, it relates to a method, device, electronic device, and computer-readable storage medium for stitching scanning electron microscope images. Background Art

[0002] A scanning electron microscope, also known as a SEM (scanning electron microscope), is an instrument that uses an electron beam to scan the surface of a sample and forms an image by collecting signals such as secondary electrons and backscattered electrons. It can provide images with very high spatial resolution, usually at the nanometer level.

[0003] Due to the limited scanning range of the electron beam, when scanning a large-sized sample, the scanning electron microscope cannot scan the entire sample or a large area at one time. Usually, it is necessary to scan local areas of the sample multiple times, and then use image stitching technology to seamlessly stitch these small-scale scanned images together to form a complete large image. However, the stitching effect of the scanning electron microscope in the related art during image stitching is poor. Summary of the Invention

[0004] The purpose of this application is to provide a method, device, electronic device, and computer-readable storage medium for stitching scanning electron microscope images, which can improve the stitching effect of the scanning electron microscope during image stitching.

[0005] In a first aspect, an embodiment of this application provides a method for stitching scanning electron microscope images, including: for each shooting point in the scanning electron microscope, obtaining a group of shooting point images taken at the shooting point, where the group of shooting point images includes multiple shooting point images with different imaging principles; for any two adjacent shooting points, determining a pair of target shooting point images with the highest similarity from the groups of shooting point images corresponding to the adjacent shooting points; and stitching the pair of target shooting point images.

[0006] Compared with related technologies, in the SEM image stitching method provided by the embodiments of the present application, for each shooting point in the scanning electron microscope, a plurality of different shooting point images taken at this shooting point based on different imaging principles form a shooting point image group corresponding to this shooting point. The different imaging principles in the scanning electron microscope mean that when the scanning electron microscope takes a picture each time, it emits a high-speed electron beam towards the sample, and these electrons will interact with the surface of the sample to generate a variety of physical signals. Each of these physical signals carries information about the surface morphology, composition, and structure of the sample. Based on different analysis methods, different images with different emphases on the same sample surface can be obtained. These images are all images of the sample surface, but only the degree of highlighting of the features on the sample surface is different. Subsequently, when stitching the images taken at adjacent shooting points, the similarity of these multiple shooting point images with different imaging principles is compared to obtain a pair of target shooting point images with the highest similarity. Thus, image comparison can be performed based on different features of the sample surface to obtain a pair of target shooting point images with the highest feature similarity and the smallest difference. Then, image stitching is performed on the pair of target shooting point images, that is, stitching is performed on the two shooting point images with the highest feature similarity and the smallest difference in the shooting point image groups taken at adjacent shooting points, thereby improving the image stitching effect.

[0007] In an alternative embodiment, the performing image stitching on the pair of target shooting point images includes: determining a target feature detection algorithm according to the imaging principle of the pair of target shooting point images; respectively extracting key point data of each target shooting point image in the pair of target shooting point images according to the target feature detection algorithm; and performing image stitching on the pair of target shooting point images based on the key point data. Since different feature detection algorithms also have differences in the feature extraction effect for images with different imaging principles, in the embodiments of the present application, a target feature detection algorithm is determined according to the imaging principle of the pair of target shooting point images, so that the feature extraction effect when performing image stitching on the pair of target shooting point images can be improved. A better feature extraction effect can make the stitching effect of the pair of target shooting point images better, thereby further improving the image stitching effect.

[0008] In an alternative embodiment, the determining a target feature detection algorithm according to the imaging principle of the pair of target shooting point images includes: providing a preset correspondence table, where the preset correspondence table includes a plurality of preset imaging principles and preset feature detection algorithms corresponding to each of the preset imaging principles; and obtaining the preset feature detection algorithm corresponding to the imaging principle of the pair of target shooting point images from the preset correspondence table as the target feature detection algorithm.

[0009] In an alternative embodiment, the preset imaging principle includes secondary electron imaging, and the preset feature detection algorithm includes the SIFT scale-invariant feature transform algorithm corresponding to the secondary electron imaging.

[0010] In an alternative embodiment, the preset imaging principle includes backscattered electron imaging, and the preset feature detection algorithm includes the ORB fast rotation brief algorithm corresponding to the backscattered electron imaging.

[0011] In an alternative embodiment, the preset imaging principle includes characteristic X-ray imaging and / or equal absorption electron imaging, and the preset feature detection algorithm includes the SURF accelerated robust feature algorithm corresponding to the characteristic X-ray imaging and / or equal absorption electron imaging.

[0012] In an alternative embodiment, determining the target pair of captured point images with the highest similarity from the groups of captured point images corresponding to adjacent captured points includes: respectively calculating the feature vectors of each captured point image in the groups of captured point images corresponding to adjacent captured points; calculating the vector distance between the feature vectors of pairs of captured point images with the same imaging principle; and using the pair of captured point images with the smallest vector distance as the target pair of captured point images.

[0013] In a second aspect, an embodiment of the present application provides a scanning electron microscope image stitching device, including: an image acquisition module, for each captured point in the scanning electron microscope, the image acquisition module is configured to acquire a group of captured point images captured at the captured point, and the group of captured point images includes multiple captured point images with different imaging principles; a target determination module, for any adjacent captured points, the target determination module is configured to determine a target pair of captured point images with the highest similarity from the groups of captured point images corresponding to the adjacent captured points; and an image stitching module, the image stitching module is configured to perform image stitching on the target pair of captured point images.

[0014] Compared with the related art, in the scanning electron microscope image stitching device provided by the embodiment of the present application, for each captured point in the scanning electron microscope, the image acquisition module acquires a group of captured point images corresponding to the captured point, which are composed of multiple different captured point images captured at the captured point based on different imaging principles. Subsequently, when stitching the images captured at adjacent captured points, the target determination module compares the similarity of these multiple captured point images with different imaging principles to obtain the target pair of captured point images with the highest similarity, so that image comparison can be performed based on different features on the surface of the sample to obtain the target pair of captured point images with the highest feature similarity and the smallest difference. Then, the image stitching module performs image stitching on the target pair of captured point images, that is, stitches the two captured point images with the highest feature similarity and the smallest difference in the groups of captured point images captured at adjacent captured points, thereby improving the image stitching effect.

[0015] In a third aspect, an embodiment of the present application provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the method as described above.

[0016] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the foregoing method is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the accompanying drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application, and thus should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a flowchart of the scanning electron microscope image stitching method provided in the first embodiment of the present application;

[0019] Figure 2 It is an imaging schematic diagram of the same sample based on different imaging principles;

[0020] Figure 3 It is a flowchart of determining a target shooting point image pair in the scanning electron microscope image stitching method provided in the first embodiment of the present application;

[0021] Figure 4 It is a structural schematic diagram of the scanning electron microscope image stitching device provided in the second embodiment of the present application;

[0022] Figure 5 It is a structural schematic diagram of the electronic device provided in the third embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Usually, the components of the embodiments of the present application described and illustrated herein can be arranged and designed in various different configurations.

[0024] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application claimed, but merely represents selected embodiments of the present application.

[0025] It should be noted that similar reference numerals and letters refer to similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0026] In addition, terms such as "first" and "second" are only used for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0027] It should be noted that, without conflict, the features in the embodiments of the present application can be combined with each other.

[0028] Embodiment 1

[0029] Embodiment 1 of the present application provides a method for stitching scanning electron microscope images, which is used to stitch the regional images of a sample taken at multiple shooting points of a scanning electron microscope to obtain a complete image of the sample. Please refer to Figure 1 , the method for stitching scanning electron microscope images provided in Embodiment 1 specifically includes the following steps.

[0030] Step S101: For each shooting point in the scanning electron microscope, obtain a group of shooting point images taken at the shooting point, and the group of shooting point images includes multiple shooting point images with different imaging principles.

[0031] Specifically, the working principle of a scanning electron microscope is to emit an electron beam to a sample and measure the relevant physical signals and physical parameters generated by the sample reflecting the electron beam, and obtain the surface image of the sample through the calculation of the physical signals and physical parameters. Since the scanning range of the electron beam is limited, for a large-sized sample, the scanning electron microscope cannot scan the entire sample at one time. Therefore, multiple shooting points are usually set in the scanning electron microscope. When scanning a large-sized sample, different regions of the sample are respectively photographed at different shooting points to obtain regional images of different regions of the sample, and finally all the regional images of the sample are stitched to obtain a complete image of the entire sample. On this basis, in this step, the scanning electron microscope respectively takes multiple shooting point images based on different imaging principles at each shooting point, and each shooting point image corresponds to one imaging principle.

[0032] Among them, different imaging principles in the scanning electron microscope mean that when the scanning electron microscope takes a picture each time, after emitting a high-speed electron beam to the sample, these electrons will interact with the surface of the sample to generate a variety of physical signals and physical parameters. These physical signals and physical parameters each carry information about the surface morphology, composition, and structure of the sample. Based on different analysis methods, different images with different emphases on the same sample surface can be obtained. These images are all images of the sample surface, but only the highlighting degrees of the features on the sample surface are different. As Figure 2 shown are three different shooting point images obtained based on different imaging principles at the same shooting point.

[0033] In this step, after obtaining multiple captured point images with different imaging principles captured at each captured point, the multiple captured point images corresponding to each captured point are formed into a captured point image group and are in one-to-one correspondence with that captured point.

[0034] Step S102: For any adjacent captured points, determine the target captured point image pair with the highest similarity from the captured point image groups corresponding to the adjacent captured points.

[0035] In this step, when splicing the images captured at any two adjacent captured points, the similarity of each captured point image in the captured point image groups corresponding to the two adjacent captured points is compared separately, and the two captured point images with the highest similarity are used as the target captured point image pair. The two captured point images in the target captured point image pair respectively correspond to the two adjacent captured points.

[0036] For example, for any two adjacent captured points a and b, the captured point a corresponds to the captured point image group A (including captured point images AX, AY, AZ..., where X, Y, Z correspond to different imaging principles), and the captured point b corresponds to the captured point image group B (including captured point images BX, BY, BZ..., where X, Y, Z correspond to different imaging principles). Determining the target captured point image pair with the highest similarity from the captured point image groups corresponding to the adjacent captured points specifically means comparing the similarity of any two captured point images in the captured point images AX, AY, AZ... and the captured point images BX, BY, BZ... respectively, and using the two captured point images with the highest similarity as the target captured point image pair.

[0037] Specifically, in the process of comparing the similarity of each captured point image in the captured point image groups corresponding to the two adjacent captured points respectively, that is, comparing the similarity of any two captured point images in the captured point images AX, AY, AZ... and the captured point images BX, BY, BZ... respectively, since the similarity of the captured point images with the same imaging principle is usually higher than that of the captured point images with different imaging principles, in some embodiments of the present application, the captured point images with the same imaging principle can be directly compared pairwise, thereby reducing the amount of computation in the process of determining the target captured point image pair and improving the splicing efficiency.

[0038] Further, in some embodiments of the present application, please refer to Figure 3 , comparing the captured point image groups of two adjacent captured points to obtain the target captured point image pair can specifically include the following steps.

[0039] Step S201: Calculate the feature vectors of each captured point image in the captured point image groups corresponding to the adjacent captured points respectively.

[0040] In this step, feature extraction and calculation are performed on each captured point image in the captured point image group corresponding to two adjacent captured points, and a feature vector corresponding to each captured point image is obtained. A feature vector is a numerical vector used to describe the features of an image. It converts various characteristics of the image into a set of numbers so that a computer can perform operations such as image analysis, recognition, classification, and retrieval on the image. The feature vector can be a one-dimensional vector composed of a series of numerical values, which can be integers, real numbers, or other data types. Each element in the vector represents the feature value of a specific aspect of the image.

[0041] In an embodiment of the present application, texture feature extraction can be performed on each captured point image based on texture feature extraction methods such as the Gray-Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), and wavelet transform, and then a feature vector is constructed based on the extracted texture features.

[0042] Specifically, the gray-level co-occurrence matrix method calculates the frequency of occurrence of pixel pairs with specific gray values and separated by specific distances and directions in the captured point image. By statistically analyzing the co-occurrence of different gray combinations, a gray-level co-occurrence matrix is obtained, and then eigenvalues such as energy, entropy, contrast, and correlation are extracted from the matrix to form the feature vector of the captured point image. The local binary pattern method can compare the gray values of the neighborhood pixels of each pixel in the image with the gray value of the pixel. Neighborhood pixels greater than or equal to the gray value of the pixel are recorded as 1, and those less than are recorded as 0. Then, these binary numbers are combined into a decimal number according to a certain rule as the local binary value of the pixel. The histogram of the local binary values of all pixels in the image is used as the feature vector of the captured point image.

[0043] Step S202: Calculate the vector distance between the feature vectors of the captured point image pairs with the same imaging principle.

[0044] In this step, the vector distance is a measure used to measure the difference or similarity between two feature vectors. In different embodiments of the present application, various different vector distance measurement methods can be used to calculate the vector distance between the feature vectors of the captured point image pairs with the same imaging principle.

[0045] For example, in some embodiments of the present application, the Euclidean distance between the feature vectors of the captured point image pairs with the same imaging principle can be calculated as the vector distance, or the Manhattan distance between the feature vectors of the captured point image pairs with the same imaging principle can also be calculated as the vector distance, or alternatively, the cosine distance between the feature vectors of the captured point image pairs with the same imaging principle can be calculated as the vector distance.

[0046] Step S203: Take the pair of captured point images with the minimum vector distance as the target pair of captured point images.

[0047] After calculating the vector distances of any two pairs of captured point images, that is, comparing the magnitudes of the vector distances of all pairs of captured point images, take the pair of captured point images with the minimum vector distance as the target pair of captured point images.

[0048] Step S103: Perform image stitching on the target pair of captured point images.

[0049] In this step, the specific operation of performing image stitching on the target pair of captured point images can be as follows: First, extract the feature points of the two target captured point images in the target pair of captured point images respectively to obtain the feature point information of each target captured point image. Then, compare the feature point information in the two target captured point images in the target pair of captured point images to obtain the overlapping area of the two target captured point images in the target pair of captured point images, and complete the image stitching of the target pair of captured point images after overlapping this overlapping area.

[0050] Among them, when extracting the feature points of the target captured point images respectively, for the target captured point images with different imaging principles, different feature detection algorithms can be used to achieve different feature point extraction effects. On this basis, in some embodiments of the present application, the corresponding target feature detection algorithm can be selected according to the imaging principle of the target captured point image for feature point extraction. That is, first determine the target feature detection algorithm according to the imaging principle of the target pair of captured point images; then extract the key point data of each target captured point image in the target pair of captured point images according to the target feature detection algorithm; finally, perform image stitching on the target pair of captured point images based on the key point data.

[0051] In some embodiments of the present application, in the process of determining the target feature detection algorithm according to the imaging principle of the target pair of captured point images, the target feature detection algorithm can be determined based on a preset correspondence table. The preset correspondence table includes multiple preset imaging principles and the corresponding preset feature detection algorithms for each preset imaging principle. When determining the target feature detection algorithm according to the imaging principle of the target pair of captured point images, the imaging principle of the target pair of captured point images can be retrieved from the multiple preset imaging principles stored in the preset correspondence table, and then according to the corresponding relationship in the preset correspondence table, determine the preset feature detection algorithm corresponding to the imaging principle of the target pair of captured point images as the target feature detection algorithm corresponding to the target pair of captured point images.

[0052] Further, in some embodiments of the present application, the preset imaging principles stored in the preset correspondence table may include, for example, secondary electron imaging. On this basis, the preset feature detection algorithms stored in the preset correspondence table may include the SIFT scale-invariant feature transform algorithm corresponding to secondary electron imaging. Among them, secondary electrons are electrons ejected from a very shallow region (usually less than 10 nm) on the surface of the sample when the electron beam bombards the sample surface. Since secondary electrons are mainly generated on the surface of the sample, they can reflect the morphological information of the sample surface. The secondary electron detector will collect these electrons and convert them into electrical signals, which are formed into an image after amplification processing. This kind of image is usually called a secondary electron image, which is mainly used to observe the surface morphology of the sample. The corresponding SIFT scale-invariant feature transform algorithm is invariant to rotation, scale scaling, and brightness changes, and has good uniqueness and rich information, which is very suitable for processing images with rich texture and clear details such as secondary electron images. The SIFT scale-invariant feature transform algorithm can extract a large number of stable feature points, which is beneficial to subsequent image stitching and matching.

[0053] In addition, in some embodiments of the present application, the preset imaging principles stored in the preset correspondence table may include, for example, backscattered electron imaging. On this basis, the preset feature detection algorithms stored in the preset correspondence table may include the ORB fast rotation brief algorithm corresponding to backscattered electron imaging. Among them, backscattered electrons are electrons that are scattered back after an elastic collision between the electron beam and the atomic nucleus in the sample. They have higher energy and can penetrate deeper sample regions. The number of backscattered electrons is closely related to the average atomic number of the sample. The higher the atomic number, the more backscattered electrons. Therefore, the backscattered electron image can reflect the compositional information of the sample and is usually called a composition contrast image or a Z-contrast image. In addition, backscattered electrons can also be used to observe the surface morphology of the sample. The backscattered electron image mainly reflects the average atomic number information of the sample. The higher the atomic number, the brighter the contrast. For the backscattered electron image, the ORB fast rotation brief algorithm combines the FAST corner detector and the BRIEF descriptor, has a faster operation speed and better rotational invariance. When the ORB fast rotation brief algorithm extracts feature points from the backscattered electron image, it can achieve fast feature detection and description while ensuring a certain stability of the feature points.

[0054] For another example, in some embodiments of the present application, the preset imaging principles stored in the preset correspondence table may include, for example, X-ray imaging. On this basis, the preset feature detection algorithms stored in the preset correspondence table may include the SURF accelerated robust feature algorithm corresponding to X-ray imaging. Among them, when an electron beam bombards a sample, if the energy of the electrons is high enough, it can excite the inner electrons of the atoms in the sample to transition to the outer layer, and at the same time release characteristic X-rays. The energy and wavelength of the characteristic X-rays are related to the type of atoms that excite them, so they can be used for elemental composition analysis. The characteristic X-ray image mainly reflects the elemental composition information of the sample and usually has a relatively complex gray-scale distribution and certain texture features. For the characteristic X-ray image, due to its certain texture features and detail information, using the SURF algorithm for feature point detection can provide a faster operation speed while ensuring stable feature detection results.

[0055] For another example, in some embodiments of the present application, the preset imaging principles stored in the preset correspondence table may include, for example, equal absorption electron images. On this basis, the preset feature detection algorithms stored in the preset correspondence table may include the SURF accelerated robust feature algorithm corresponding to equal absorption electron images. Among them, the absorption electron image refers to when the incident electrons enter the specimen, after multiple inelastic scatterings, their energy is exhausted. If the thickness of the specimen is much larger than the penetration depth of the transmitted electrons, these electrons will eventually be absorbed by the specimen. If a highly sensitive ammeter is connected between the specimen and the ground, the current signal of the specimen to the ground can be measured, and this signal current comes from the absorption electrons. If the absorption electrons are collected and then amplified and modulated to form an image, an absorption electron image can be formed. The equal absorption electron image is usually related to the thickness and density of the sample and has a relatively complex gray-scale distribution and certain texture features. For the equal absorption electron image, using the SURF algorithm for feature point detection can provide a faster operation speed while ensuring stable feature detection results.

[0056] It can be understood that the foregoing is only an example illustration of the preset imaging principles and preset feature detection algorithms stored in the preset correspondence table, and the corresponding relationship between the two in some embodiments of the present application, and does not constitute a limitation. In some other embodiments of the present application, other imaging principles and corresponding feature detection algorithms may also be included.

[0057] Compared with the related art, in the SEM image stitching method provided by the embodiments of the present application, for each shooting point in the scanning electron microscope, a plurality of different shooting point images taken at this shooting point based on different imaging principles form the shooting point image group corresponding to this shooting point. When stitching the images taken at adjacent shooting points, the similarity of these multiple shooting point images with different imaging principles is compared to obtain the target shooting point image pair with the highest similarity. Thus, image comparison can be performed based on different features on the sample surface to obtain the target shooting point image pair with the highest feature similarity and the smallest difference. Then, image stitching is performed on the target shooting point image pair, that is, the two shooting point images with the highest feature similarity and the smallest difference in the shooting point image groups taken at adjacent shooting points are stitched, thereby improving the image stitching effect.

[0058] Embodiment 2

[0059] Embodiment 2 of the present application provides a scanning electron microscope image stitching device, as Figure 4 shown, including: an image acquisition module 401. For each shooting point in the scanning electron microscope, the image acquisition module 401 is used to acquire the shooting point image group taken at the shooting point. The shooting point image group includes a plurality of shooting point images with different imaging principles; a target determination module 402. For any adjacent shooting points, the target determination module 402 is used to determine the target shooting point image pair with the highest similarity from the shooting point image groups corresponding to the adjacent shooting points; an image stitching module 403. The image stitching module 403 is used to perform image stitching on the target shooting point image pair.

[0060] It can be understood that the scanning electron microscope image stitching device provided by Embodiment 2 of the present application is the embodiment corresponding to the device in Embodiment 1 described above. The technical details provided in this Embodiment 2 can be correspondingly applied to the scanning electron microscope image stitching method provided in Embodiment 1 described above, and the technical details provided in Embodiment 1 described above can also be correspondingly applied to the scanning electron microscope image stitching device provided in this Embodiment 2.

[0061] Furthermore, the scanning electron microscope image stitching device provided in this Embodiment 2 can specifically be a host computer, a server, etc. independent of the scanning electron microscope, or an electronic device provided inside the scanning electron microscope.

[0062] Compared with the related art, in the scanning electron microscope image stitching device provided in the second embodiment of the present application, for each shooting point in the scanning electron microscope, the image acquisition module 401 acquires a plurality of different shooting point images taken at this shooting point based on different imaging principles to form a shooting point image group corresponding to this shooting point. Subsequently, when stitching the images taken at adjacent shooting points, the target determination module 402 compares the similarities of these multiple shooting point images with different imaging principles to obtain the target shooting point image pair with the highest similarity. Thus, image comparison can be performed based on different features on the sample surface to obtain the target shooting point image pair with the highest feature similarity and the smallest difference. Then, the image stitching module 403 stitches the target shooting point image pair, that is, stitches the two shooting point images with the highest feature similarity and the smallest difference in the shooting point image groups taken at adjacent shooting points, thereby improving the image stitching effect.

[0063] Embodiment Three

[0064] Embodiment Three of the present application relates to an electronic device, as Figure 5 shown, including: at least one processor 501; and a memory 502 communicatively connected to the at least one processor 501; wherein, the memory 502 stores instructions executable by the at least one processor 501, and the instructions are executed by the at least one processor 501 to enable the at least one processor 501 to execute the methods in the above embodiments.

[0065] Among them, the memory and the processor are connected by a bus. The bus can include any number of interconnected buses and bridges. The bus connects various circuits of one or more processors and the memory together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art. Therefore, they will not be further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, and provides a unit for communicating with various other devices on the transmission medium. The data processed by the processor is transmitted over the wireless medium through the antenna. Further, the antenna also receives data and transmits the data to the processor.

[0066] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interface, voltage regulation, power management, and other control functions. The memory can be used to store the data used by the processor when performing operations.

[0067] Embodiment Four

[0068] Embodiment Four of the present application relates to a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method embodiments described above are implemented.

[0069] That is, those skilled in the art can understand that all or part of the steps in implementing the methods of the above embodiments can be completed by instructing relevant hardware through a program. This program is stored in a storage medium and includes several instructions for causing a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the methods of various embodiments of the present application. The foregoing storage medium includes: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0070] The above are only specific implementation manners of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for stitching scanning electron microscope images, characterized in that Including: For each shooting point in the scanning electron microscope, obtain a group of shooting point images taken at the shooting point, where the group of shooting point images includes multiple shooting point images with different imaging principles; For any adjacent shooting points, determine the pair of target shooting point images with the highest similarity from the group of shooting point images corresponding to the adjacent shooting points; Perform image stitching on the pair of target shooting point images; The performing image stitching on the pair of target shooting point images includes: Determine a target feature detection algorithm according to the imaging principle of the pair of target shooting point images; Extract the key point data of each target shooting point image in the pair of target shooting point images respectively according to the target feature detection algorithm; Perform image stitching on the pair of target shooting point images based on the key point data; The determining a target feature detection algorithm according to the imaging principle of the pair of target shooting point images includes: Provide a preset correspondence table, where the preset correspondence table includes multiple preset imaging principles and preset feature detection algorithms corresponding to each of the preset imaging principles; Obtain the preset feature detection algorithm corresponding to the imaging principle of the pair of target shooting point images from the preset correspondence table as the target feature detection algorithm.

2. The method for stitching scanning electron microscope images according to claim 1, wherein The preset imaging principle includes secondary electron imaging, and the preset feature detection algorithm includes the SIFT scale-invariant feature transform algorithm corresponding to the secondary electron imaging.

3. The method for stitching scanning electron microscope images according to claim 1, characterized in that The preset imaging principle includes backscattered electron imaging, and the preset feature detection algorithm includes the ORB fast rotation brief algorithm corresponding to the backscattered electron imaging.

4. The method for stitching scanning electron microscope images according to claim 1, characterized in that, The preset imaging principle includes characteristic X-ray imaging and / or equal absorption electron image, and the preset feature detection algorithm includes the SURF accelerated robust feature algorithm corresponding to the characteristic X-ray imaging and / or equal absorption electron image.

5. The method for stitching scanning electron microscope images according to claim 1, wherein The determining the pair of target shooting point images with the highest similarity from the group of shooting point images corresponding to the adjacent shooting points includes: Calculate the feature vectors of each shooting point image in the group of shooting point images corresponding to the adjacent shooting points respectively; Calculate the vector distance of the feature vectors of the pair of shooting point images with the same imaging principle; Take the pair of shooting point images with the smallest vector distance as the pair of target shooting point images.

6. A scanning electron microscope image stitching device, characterized in that Including: An image acquisition module. For each shooting point in the scanning electron microscope, the image acquisition module is used to obtain a group of shooting point images taken at the shooting point, where the group of shooting point images includes multiple shooting point images with different imaging principles; A target determination module. For any adjacent shooting points, the target determination module is used to determine the pair of target shooting point images with the highest similarity from the group of shooting point images corresponding to the adjacent shooting points; An image stitching module. The image stitching module is used to perform image stitching on the pair of target shooting point images; The performing image stitching on the pair of target shooting point images includes: Determine a target feature detection algorithm according to the imaging principle of the pair of target shooting point images; Extract the key point data of each target shooting point image in the pair of target shooting point images respectively according to the target feature detection algorithm; Perform image stitching on the pair of target shooting point images based on the key point data; Determining the target feature detection algorithm according to the imaging principle of the target pair of captured images includes: Providing a preset correspondence table, where the preset correspondence table includes a plurality of preset imaging principles and preset feature detection algorithms corresponding to each of the preset imaging principles; Obtaining, from the preset correspondence table, the preset feature detection algorithm corresponding to the imaging principle of the target pair of captured images as the target feature detection algorithm.

7. An electronic device, characterized in that, It includes: At least one processor; And a memory communicatively connected to the at least one processor; Wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, The computer program is executed by a processor to implement the method according to any one of claims 1 to 5.

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