Scanning electron microscope image splicing method and device, electronic equipment and storage medium

Abstract

The invention relates to the field of image processing, and discloses a scanning electron microscope image splicing method and device, electronic equipment and a computer readable storage medium. The scanning electron microscope image splicing method comprises the steps that for each shooting point in a scanning electron microscope, a shooting point image group shot at the shooting point is acquired, and the shooting point image group comprises a plurality of shooting point images of different imaging principles; for any adjacent shooting point, determining a target shooting point image pair with the highest similarity from the shooting point image group corresponding to the adjacent shooting point; and carrying out image splicing on the target shooting point image pair. Compared with the prior art, the scanning electron microscope image splicing method and device, the electronic equipment and the computer readable storage medium provided by the embodiment of the invention have the advantage that the splicing effect of the scanning electron microscope during image splicing can be improved.

Description

Technical Field

[0001] The present application relates to the field of image processing, and in particular to a scanning electron microscope image stitching method, device, electronic device and computer-readable storage medium. Background Art

[0002] A scanning electron microscope (SEM) is an instrument that uses an electron beam to scan the surface of a sample and collects signals such as secondary electrons and backscattered electrons to form an image. 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 large-size samples, the scanning electron microscope cannot scan the entire sample or a large area at one time. It is usually necessary to scan the local area of ​​the sample multiple times, and then these small-range scanned images are seamlessly stitched together through image stitching technology to form a complete large image. However, the scanning electron microscope in the related art has a poor stitching effect when performing image stitching. Summary of the invention

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

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

[0006] Compared with the related art, in the scanning electron microscope image stitching method provided in the embodiment of the present application, for each shooting point in the scanning electron microscope, a plurality of different shooting point images shot at the shooting point based on different imaging principles constitute a shooting point image group corresponding to the shooting point. The different imaging principles in the scanning electron microscope are that the scanning electron microscope emits a high-speed electron beam to the sample each time it takes a picture. These electrons interact with the sample surface to generate a variety of physical signals. Each of these physical signals carries information about the sample surface morphology, composition and structure. Different images with different focuses on the same sample surface can be obtained based on different analysis methods. These The images are all images of the sample surface, but the degree of prominence of the sample surface features is different. When the images taken at adjacent shooting points are subsequently stitched together, the multiple shooting point images with different imaging principles are compared for similarity to obtain the target shooting point image pair with the highest similarity. This allows image comparison based on different features of the sample surface to obtain the target shooting point image pair with the highest feature similarity and the smallest difference. The target shooting point image pair is then stitched together, that is, the two shooting point images with the highest feature similarity and the smallest difference in the shooting point image group taken at adjacent shooting points are stitched together, thereby improving the image stitching effect.

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

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

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

[0010] In an optional embodiment, the preset imaging principle includes backscattered electron imaging, and the preset feature detection algorithm includes an ORB rapid rotation briefing algorithm corresponding to the backscattered electron imaging.

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

[0012] In an optional embodiment, determining the target shooting point image pair with the highest similarity from the shooting point image group corresponding to the adjacent shooting points includes: respectively calculating the feature vector of each shooting point image in the shooting point image group corresponding to the adjacent shooting points; calculating the vector distance of the feature vectors of the shooting point image pair with the same imaging principle; and taking the shooting point image pair with the smallest vector distance as the target shooting point image pair.

[0013] In a second aspect, an embodiment of the present application provides a scanning electron microscope image stitching device, comprising: an image acquisition module, for each shooting point in the scanning electron microscope, the image acquisition module is used to acquire a shooting point image group shot at the shooting point, the shooting point image group including multiple shooting point images of different imaging principles; a target determination module, for any adjacent shooting points, the target determination module is used to determine a 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, the image stitching module is used to perform image stitching on the target shooting point image pair.

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

[0015] In a third aspect, an embodiment of the present application provides an electronic device, comprising: 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 as described above.

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

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

[0018] Figure 1 A schematic diagram of the flow of the scanning electron microscope image stitching method provided in Example 1 of the present application; Figure 2 Schematic diagram of imaging of the same sample based on different imaging principles; Figure 3 A schematic diagram of a process for determining a target shooting point image pair in the scanning electron microscope image stitching method provided in the first embodiment of the present application; Figure 4 A schematic diagram of the structure of a scanning electron microscope image stitching device provided in Example 2 of the present application; Figure 5 This is a schematic diagram of the structure of the electronic device provided in Example 3 of the present application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application as claimed, but is merely representative of selected embodiments of the present application.

[0021] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0022] In addition, the terms “first”, “second”, etc., if used, are merely used to distinguish between the descriptions and should not be understood as indicating or implying relative importance.

[0023] It should be noted that, in the absence of conflict, the features in the embodiments of the present application may be combined with each other.

[0024] The first embodiment of the present application provides a scanning electron microscope image stitching method for stitching regional images of a sample taken at multiple shooting points of a scanning electron microscope to obtain a complete image of the sample. Figure 1 The scanning electron microscope image stitching method provided in the first embodiment specifically includes the following steps.

[0025] Step S101: for each shooting point in the scanning electron microscope, a shooting point image group shot at the shooting point is acquired, wherein the shooting point image group includes a plurality of shooting point images with different imaging principles.

[0026] Specifically, the working principle of the scanning electron microscope is to emit an electron beam to the sample, measure the relevant physical signals and physical parameters generated by the sample reflecting the electron beam, and obtain the surface image of the sample by calculating the physical signals and physical parameters. Due to the limited scanning range of the electron beam, for large-sized samples, the scanning electron microscope cannot scan the entire sample at one time, so a plurality of shooting points are usually provided in the scanning electron microscope. When scanning large-sized samples, different areas of the sample are photographed at different shooting points to obtain regional images of different areas of the sample, and finally all regional images of the sample are spliced ​​to obtain a complete image of the entire sample. On this basis, in this step, the scanning electron microscope takes multiple shooting point images at each shooting point based on different imaging principles, and each shooting point image corresponds to an imaging principle.

[0027] Among them, the different imaging principles in the scanning electron microscope are that each time the scanning electron microscope shoots a high-speed electron beam to the sample, these electrons will interact with the sample surface to generate a variety of physical signals and physical parameters. These physical signals and physical parameters each carry information about the sample surface morphology, composition and structure. Based on different analysis methods, different images with different focuses on the same sample surface can be obtained. These images are all images of the sample surface, but the degree of highlighting the characteristics of the sample surface is different. Figure 2 Shown are three different shooting point images obtained at the same shooting point based on different imaging principles.

[0028] In this step, after a plurality of shooting point images of different imaging principles taken at each shooting point are acquired, the plurality of shooting point images corresponding to each shooting point are grouped into a shooting point image group corresponding to the shooting point one by one.

[0029] Step S102: for any adjacent shooting points, determine a target shooting point image pair with the highest similarity from the shooting point image group corresponding to the adjacent shooting points.

[0030] In this step, when stitching images taken at any two adjacent shooting points, each shooting point image in the shooting point image group corresponding to the two adjacent shooting points is compared for similarity, and the two shooting point images with the highest similarity are obtained as the target shooting point image pair, and the two shooting point images in the target shooting point image pair correspond to the two adjacent shooting points respectively.

[0031] For example, for any two adjacent shooting points a and b, shooting point a corresponds to shooting point image group A (including shooting point images AX, AY, AZ..., X, Y, Z corresponding to different imaging principles), and shooting point b corresponds to shooting point image group B (including shooting point images BX, BY, BZ..., X, Y, Z corresponding to different imaging principles). Determining the target shooting point image pair with the highest similarity from the shooting point image groups corresponding to the adjacent shooting points is specifically to compare the similarities of any two shooting point images in the shooting point images AX, AY, AZ... and the shooting point images BX, BY, BZ..., and taking the two shooting point images with the highest similarity as the target shooting point image pair.

[0032] Specifically, in the process of performing a similarity comparison on each shooting point image in the shooting point image group corresponding to two adjacent shooting points, that is, comparing the similarity of any two shooting point images among the shooting point images AX, AY, AZ... and the shooting point images BX, BY, BZ..., since the similarity of shooting point images with the same imaging principle is usually higher than that of shooting point images with different imaging principles, in some embodiments of the present application, the shooting point images with the same imaging principle can be directly compared in pairs, thereby reducing the amount of calculation in the process of determining the target shooting point image pair and improving the stitching efficiency.

[0033] Further, in some embodiments of the present application, please refer to Figure 3 , comparing the shooting point image groups of two adjacent shooting points to obtain the target shooting point image pair may specifically include the following steps.

[0034] Step S201: Calculate the feature vectors of each shooting point image in the shooting point image group corresponding to adjacent shooting points respectively.

[0035] In this step, feature extraction and calculation are performed on each shooting point image in the shooting point image group corresponding to two adjacent shooting points to obtain a feature vector corresponding to each shooting point image. A feature vector is a numerical vector used to describe image features. It converts various characteristics of an image into a set of numbers so that a computer can perform operations such as analysis, recognition, classification and retrieval on the image. A feature vector can be a one-dimensional vector consisting of a series of numerical values, which can be integers, real numbers or other data types. Each element in the vector represents a feature value of a specific aspect of the image.

[0036] In an embodiment of the present application, texture features may be extracted from each shooting point image based on a texture feature extraction method such as a gray-level co-occurrence matrix (GLCM), a local binary pattern (LBP), or a wavelet transform, and a feature vector may be constructed based on the extracted texture features.

[0037] Specifically, the grayscale co-occurrence matrix method calculates the frequency of pixel pairs with specific grayscale values ​​and specific distances and directions in the shooting point image. By counting the co-occurrence of different grayscale combinations, the grayscale co-occurrence matrix is ​​obtained, and then the energy, entropy, contrast, correlation and other eigenvalues ​​are extracted from the matrix to form the eigenvector of the shooting point image. The local binary pattern method can compare the grayscale values ​​of its neighboring pixels with the grayscale value of the pixel for each pixel in the image. The neighboring pixels that are greater than or equal to the grayscale value of the pixel are recorded as 1, and those that are less than the grayscale value are recorded as 0. Then, these binary numbers are combined into a decimal number according to certain rules as the local binary value of the pixel. The histogram of the local binary values ​​of all pixels in the image is counted as the eigenvector of the shooting point image.

[0038] Step S202: Calculate the vector distance of the feature vectors of the pair of shooting point images with the same imaging principle.

[0039] In this step, vector distance is a measure used to measure the difference or similarity between two feature vectors. In different embodiments of the present application, a variety of different vector distance measurement methods can be used to calculate the vector distance of feature vectors of a pair of shooting point images with the same imaging principle.

[0040] For example, in some embodiments of the present application, the Euclidean distance of the feature vectors of the image pair of shooting points with the same imaging principle can be calculated as the vector distance, or the Manhattan distance of the feature vectors of the image pair of shooting points with the same imaging principle can be calculated as the vector distance, or the cosine distance of the feature vectors of the image pair of shooting points with the same imaging principle can be calculated as the vector distance.

[0041] Step S203: taking the shooting point image pair with the smallest vector distance as the target shooting point image pair.

[0042] After calculating the vector distance between any two shooting point image pairs, the vector distances of all shooting point image pairs are compared, and the shooting point image pair with the smallest vector distance is taken as the target shooting point image pair.

[0043] Step S103: performing image stitching on the target shooting point image pair.

[0044] In this step, the target shooting point images are stitched together by first extracting feature points from the two target shooting point images in the target shooting point image pair, respectively, to obtain feature point information of each target shooting point image. Then, the feature point information of the two target shooting point images in the target shooting point image pair is compared to obtain a repeated area of ​​the two target shooting point images in the target shooting point image pair, and the image stitching of the target shooting point image pair is completed after the repeated area is overlapped.

[0045] Among them, when extracting feature points from target shooting point images respectively, different feature point extraction effects can be achieved by using different feature detection algorithms for target shooting point images with different imaging principles. 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 shooting point image to extract feature points. That is, firstly, the target feature detection algorithm is determined according to the imaging principle of the target shooting point image pair; then, the key point data of each target shooting point image in the target shooting point image pair is extracted respectively according to the target feature detection algorithm; finally, the target shooting point image pair is image stitched based on the key point data.

[0046] 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 shooting point image pair, the target feature detection algorithm can be determined based on a preset corresponding table. The preset corresponding table includes multiple preset imaging principles and preset feature detection algorithms corresponding to each preset imaging principle. When determining the target feature detection algorithm according to the imaging principle of the target shooting point image pair, the imaging principle of the target shooting point image pair can be retrieved from the multiple preset imaging principles stored in the preset corresponding table, and then according to the corresponding relationship in the preset corresponding table, the preset feature detection algorithm corresponding to the imaging principle of the target shooting point image pair is determined as the target feature detection algorithm corresponding to the target shooting point image pair.

[0047] Further, in some embodiments of the present application, the preset imaging principle stored in the preset corresponding table may include, for example, secondary electron imaging. On this basis, the preset feature detection algorithm stored in the preset corresponding table may include the SIFT scale-invariant feature transformation algorithm corresponding to the secondary electron imaging. Among them, secondary electrons are electrons ejected from a very shallow area (usually less than 10 nm) on the surface of the sample when the electron beam bombards the surface of the sample. Since secondary electrons are mainly generated on the surface of the sample, they can reflect the morphological information of the surface of the sample. The secondary electron detector collects these electrons and converts them into electrical signals, which are then amplified to form an image. This type 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 transformation algorithm remains invariant to rotation, scale scaling, and brightness changes, and has good uniqueness and rich information. It is very suitable for processing images with rich textures and clear details such as secondary electron images. The SIFT scale-invariant feature transformation algorithm can extract a large number of stable feature points, which is beneficial to subsequent image stitching and matching.

[0048] In addition, in some embodiments of the present application, the preset imaging principle stored in the preset corresponding table may include, for example, backscattered electron imaging. On this basis, the preset feature detection algorithm stored in the preset corresponding table may include an ORB fast rotation briefing algorithm corresponding to backscattered electron imaging. Among them, backscattered electrons are electrons that are backscattered back after the electron beam elastically collides with the atomic nuclei in the sample. They have higher energy and can penetrate deeper sample areas. 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 composition information of the sample, which is usually referred to as 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 backscattered electron images, the ORB fast rotation briefing algorithm combines the FAST corner detector and the BRIEF descriptor, and has a faster computing speed and better rotation invariance. When extracting feature points from backscattered electron images, the ORB fast rotation briefing algorithm can achieve fast feature detection and description while ensuring a certain stability of feature points.

[0049] For another example, in some embodiments of the present application, the preset imaging principle stored in the preset corresponding table may include, for example, X-ray imaging. On this basis, the preset feature detection algorithm stored in the preset corresponding table may include a SURF accelerated robust feature algorithm corresponding to backscattered electron imaging. Among them, when the electron beam bombards the sample, if the energy of the electron is high enough, it can excite the inner electrons of the atoms in the sample to transition to the outer layer, while releasing characteristic X-rays. The energy and wavelength of the characteristic X-rays are related to the type of atoms that excite it, 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 more complex grayscale distribution and certain texture features. For the characteristic X-ray image, because it contains certain texture features and detail information, the use of the SURF algorithm for feature point detection can provide a faster computing speed while ensuring stable feature detection results.

[0050] For another example, in some embodiments of the present application, the preset imaging principle stored in the preset corresponding table may include an isoabsorption electron image, on this basis, the preset feature detection algorithm stored in the preset corresponding table may include a SURF accelerated robust feature algorithm corresponding to the isoabsorption electron image. Among them, the absorption electron image refers to when the incident electron enters the sample, after multiple inelastic scatterings, the energy is completely lost. If the thickness of the sample far exceeds the penetration depth of the transmitted electron, these electrons will eventually be absorbed by the sample. If a high-sensitivity ammeter is connected between the sample and the ground, the current signal of the sample to the ground can be measured, and this signal current comes from the absorption electrons. If the absorbed electrons are collected and then amplified and modulated to form an image, an absorption electron image can be formed. The isoabsorption electron image is usually related to the thickness and density of the sample, and has a more complex grayscale distribution and certain texture characteristics. For the isoabsorption electron image, the SURF algorithm is used for feature point detection, which can provide a faster computing speed while ensuring stable feature detection results.

[0051] It can be understood that the foregoing is only an example of the preset imaging principles and preset feature detection algorithms stored in the preset correspondence table and the correspondence 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.

[0052] Compared with the related art, in the scanning electron microscope image stitching method provided in the embodiment of the present application, for each shooting point in the scanning electron microscope, multiple different shooting point images shot at the shooting point based on different imaging principles constitute a shooting point image group corresponding to the shooting point, and when stitching images shot at adjacent shooting points, the multiple shooting point images with different imaging principles are compared for similarity to obtain a target shooting point image pair with the highest similarity, so that image comparison can be performed based on different features of the sample surface to obtain a target shooting point image pair with the highest feature similarity and the smallest difference, and 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 group shot at adjacent shooting points are stitched, thereby improving the image stitching effect.

[0053] Embodiment 2 of the present application provides a scanning electron microscope image stitching device, such as Figure 4 As shown, it includes: an image acquisition module 401, for each shooting point in the scanning electron microscope, the image acquisition module 401 is used to acquire a shooting point image group shot at the shooting point, and the shooting point image group includes multiple 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 stitch the target shooting point image pair.

[0054] It can be understood that the scanning electron microscope image stitching device provided in the second embodiment of the present application is an embodiment of the device corresponding to the aforementioned embodiment one, and the technical details provided in the second embodiment can be applied to the scanning electron microscope image stitching method provided in the aforementioned embodiment one, and the technical details provided in the aforementioned embodiment one can also be applied to the scanning electron microscope image stitching device provided in the second embodiment.

[0055] Furthermore, the scanning electron microscope image stitching device provided in the second embodiment may specifically be a host computer, server or other equipment independent of the scanning electron microscope, or may be an electronic device disposed inside the scanning electron microscope.

[0056] 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 multiple different shooting point images shot at the shooting point based on different imaging principles to form a shooting point image group corresponding to the shooting point. When stitching the images shot at adjacent shooting points later, the target determination module 402 compares the similarity of the multiple shooting point images of these different imaging principles to obtain the target shooting point image pair with the highest similarity, so that image comparison can be performed based on different features of 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 performs image stitching on 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 group shot at adjacent shooting points, thereby improving the image stitching effect.

[0057] Embodiment 3 of the present application relates to an electronic device, such as Figure 5 As shown, it includes: at least one processor 501; and a memory 502 that is communicatively connected to the at least one processor 501; wherein the memory 502 stores instructions that can be executed by the at least one processor 501, and the instructions are executed by the at least one processor 501 so that the at least one processor 501 can execute the methods in the above-mentioned embodiments.

[0058] Among them, the memory and the processor are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors and memories together. The bus can also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not 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, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor is transmitted on a wireless medium via an antenna, and further, the antenna also receives data and transmits the data to the processor.

[0059] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory can be used to store data used by the processor when performing operations.

[0060] Embodiment 4 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 above method embodiment is implemented.

[0061] That is, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0062] The above are only specific implementations 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 a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A scanning electron microscope image stitching method, characterized in that: include: For each shooting point in the scanning electron microscope, a shooting point image group shot at the shooting point is acquired, wherein the shooting point image group includes a plurality of shooting point images with different imaging principles; For any adjacent shooting points, determining a target shooting point image pair with the highest similarity from the shooting point image groups corresponding to the adjacent shooting points; Image stitching is performed on the target shooting point image pairs.

2. The scanning electron microscope image stitching method according to claim 1, characterized in that: The image stitching of the target shooting point image pair comprises: Determine a target feature detection algorithm according to the imaging principle of the target shooting point image pair; Extracting key point data of each target shooting point image in the target shooting point image pair respectively according to the target feature detection algorithm; Image stitching is performed on the target shooting point image pair based on the key point data.

3. The scanning electron microscope image stitching method according to claim 2, characterized in that: The step of determining a target feature detection algorithm according to the imaging principle of the target shooting point image pair comprises: Providing a preset correspondence table, the preset correspondence table including a plurality of preset imaging principles and a preset feature detection algorithm corresponding to each of the preset imaging principles; A preset feature detection algorithm corresponding to the imaging principle of the target shooting point image pair is obtained from the preset corresponding table as the target feature detection algorithm.

4. The scanning electron microscope image stitching method according to claim 3, characterized in that: The preset imaging principle includes secondary electron imaging, and the preset feature detection algorithm includes a SIFT scale-invariant feature transformation algorithm corresponding to the secondary electron imaging.

5. The scanning electron microscope image stitching method according to claim 3, characterized in that: The preset imaging principle includes backscattered electron imaging, and the preset feature detection algorithm includes an ORB rapid rotation briefing algorithm corresponding to the backscattered electron imaging.

6. The scanning electron microscope image stitching method according to claim 3, characterized in that: The preset imaging principle includes characteristic X-ray imaging and / or isoabsorption electron imaging, and the preset feature detection algorithm includes a SURF accelerated robust feature algorithm corresponding to the characteristic X-ray imaging and / or isoabsorption electron imaging.

7. The scanning electron microscope image stitching method according to claim 1, characterized in that: The step of determining the target shooting point image pair with the highest similarity from the shooting point image groups corresponding to the adjacent shooting points includes: respectively calculating the feature vectors of each shooting point image in the shooting point image group corresponding to the adjacent shooting points; Calculate the vector distance of the feature vectors of the image pair of shooting points with the same imaging principle; The shooting point image pair with the smallest vector distance is used as the target shooting point image pair.

8. A scanning electron microscope image stitching device, characterized in that: include: An image acquisition module, for each shooting point in the scanning electron microscope, the image acquisition module is used to acquire a shooting point image group shot at the shooting point, the shooting point image group including a plurality of shooting point images with different imaging principles; A target determination module, for any adjacent shooting points, the target determination module is used to determine a target shooting point image pair with the highest similarity from the shooting point image group corresponding to the adjacent shooting points; An image stitching module is used to stitch the target shooting point image pairs.

9. An electronic device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed 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 7.

10. 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 7.

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