Shale FIB-SEM image processing method and device, equipment and storage medium

Abstract

The invention provides a shale FIB-SEM image processing method and device, equipment and a storage medium, and belongs to the field of petroleum geology. The method comprises the following steps: acquiring a to-be-processed FIB-SEM image of a shale sample; performing horizontal edge preserving filtering on the FIB-SEM image to be processed to obtain a horizontal filtering result; and decomposing and removing vertical bar noise based on a horizontal filtering result to obtain a target FIB-SEM image of the rock sample. Horizontal edge preserving filtering and vertical bar noise decomposition are carried out on a shale FIB-SEM image in sequence, vertical bar noise is decomposed and eliminated under the condition that the stability of a horizontal edge is ensured, and an improved shale FIB-SEM image is obtained. For a shale FIB-SEM image with a large amount of strip-shaped noise, the negative influence of stripe defects formed in the imaging process on pore structure extraction can be solved, and the fine representation accuracy of a shale gas reservoir is effectively improved.

Description

Technical Field

[0001] The present application relates to the field of petroleum geology, and in particular to a shale FIB-SEM image processing method, device, equipment and storage medium. Background Art

[0002] Compared with conventional resources, the characteristic scale of shale gas reservoirs is concentrated at the micro-nano level. With the gradual refinement of unconventional gas reservoir exploration, the industry has developed a variety of micro-nanoscale characterization techniques, among which the most representative experiment that can achieve three-dimensional spatialization is the Focused Ion Beam-Scanning Electron Microscope (FIB-SEM). Current research on FIB-SEM experiments mainly focuses on two aspects: pore structure characterization and image segmentation quality improvement.

[0003] However, due to the strong heterogeneity of shale, vertical scratches will be formed on the surface during the imaging process, resulting in strip noise in the FIB-SEM image, which has a great negative impact on subsequent image processing and pore structure extraction. Summary of the invention

[0004] The present application provides a shale FIB-SEM image processing method, device, equipment and storage medium, which can effectively remove strip noise in shale FIB-SEM images and improve the accuracy of pore structure recognition. The technical solution is as follows:

[0005] On the one hand, an embodiment of the present application provides a method for processing a shale FIB-SEM image, comprising:

[0006] Obtaining FIB-SEM images of the shale samples to be processed;

[0007] Performing horizontal edge-preserving filtering on the FIB-SEM image to be processed to obtain a horizontal filtering result;

[0008] Based on the horizontal filtering result, vertical strip noise is decomposed and removed to obtain a target FIB-SEM image of the rock sample.

[0009] On the other hand, an embodiment of the present application provides a shale FIB-SEM image processing device, comprising:

[0010] An acquisition module, used for acquiring a focused ion beam scanning electron microscope FIB-SEM image of the shale rock sample to be processed;

[0011] A filtering module, used for performing horizontal edge-preserving filtering on the FIB-SEM image to be processed to obtain a horizontal filtering result;

[0012] A decomposition module is used to decompose and remove vertical strip noise based on the horizontal filtering result to obtain a target FIB-SEM image of the rock sample.

[0013] On the other hand, an embodiment of the present application provides an electronic device, which includes a memory and a processor; a computer program is stored in the memory, and when the computer program is executed by the processor, the method described in the above aspects is implemented.

[0014] On the other hand, an embodiment of the present application provides a computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program is loaded and executed by a processor to implement the method as described in the above aspects.

[0015] The technical solution provided by this application includes at least the following beneficial effects:

[0016] The present application provides a shale FIB-SEM image processing method, device, equipment and storage medium. By successively performing horizontal edge retention filtering and vertical strip noise decomposition on the shale FIB-SEM image, the vertical strip noise is decomposed and removed while ensuring the stability of the horizontal edge, and an improved shale FIB-SEM image is obtained. For shale FIB-SEM images with a large amount of strip noise, the negative impact of stripe defects formed during the imaging process on the extraction of pore structure can be solved, and the accuracy of fine characterization of shale gas reservoirs can be effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments.

[0018] Figure 1 is a flow chart of a method for processing shale FIB-SEM images provided by an exemplary embodiment of the present application;

[0019] Figure 2 is a flow chart of a method for processing shale FIB-SEM images provided by another exemplary embodiment of the present application;

[0020] Figure 3 is a flow chart of a method for processing shale FIB-SEM images provided by another exemplary embodiment of the present application;

[0021] Figure 4 is a FIB-SEM image to be processed provided by an exemplary embodiment of the present application;

[0022] Figure 5 This is a schematic diagram of the effect of directly performing threshold segmentation using a FIB-SEM image to be processed, provided by an exemplary embodiment of the present application;

[0023] Figure 6 This is an effect diagram of an exemplary embodiment of the present application, which uses a target FIB-SEM image to first undergo one-dimensional guided filtering and then threshold segmentation;

[0024] Figure 7 It is a threshold segmentation effect diagram provided by an exemplary embodiment of the present application, in which a target FIB-SEM image is first subjected to median filtering and one-dimensional guided filtering and then to threshold segmentation;

[0025] Figure 8 It is a final effect diagram after the target FIB-SEM image is improved in image quality through the process of the present invention, provided by an exemplary embodiment of the present application;

[0026] Fig. 9 is a structural block diagram of a shale FIB-SEM image processing device provided by an exemplary embodiment of the present application;

[0027] Fig.10 It is a structural block diagram of an electronic device provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0029] With the gradual refinement of unconventional gas reservoir exploration, a variety of micro-nanoscale characterization techniques have been developed, among which the most representative experiment that can achieve three-dimensional spatialization is focused ion beam scanning electron microscopy. At present, the relevant research on FIB-SEM experiments mainly focuses on two aspects: pore structure characterization and image segmentation quality improvement.

[0030] (1) Pore structure characterization: Based on FIB-SEM experimental data, the pore structure model can be established by using three-dimensional digital core technology, and parameters such as pore size, specific surface area, and pore-throat ratio can be extracted for quantitative characterization. However, there are still many problems with threshold segmentation technology and image noise, which will cause error terms when quantifying pore structure parameters.

[0031] (2) Improved image segmentation quality: The quantitative analysis of micro-nano pore structures has high requirements for image quality. FIB-SEM images will be disturbed by various factors during the experiment, and there will be noise in the final imaging effect. At present, the improvement of FIB-SEM image segmentation quality is mainly reflected in two aspects. One is image noise reduction. Commonly used noise reduction methods include median filtering, Gaussian filtering, mean filtering, etc., which can effectively remove salt and pepper noise. On the other hand, it is reflected in the improvement of threshold segmentation algorithm. At present, there are studies combining intelligent algorithms (such as convolutional neural networks) to improve FIB-SEM image segmentation and achieve accurate segmentation. However, the limitations of the above two aspects are that they are ineffective for stripe defects generated during FIB-SEM imaging.

[0032] Embodiment 1

[0033] Please refer to Figure 1 , which shows a flow chart of a method for processing shale FIB-SEM images provided by an exemplary embodiment of the present application. The method comprises the following steps:

[0034] Step 101, obtaining a FIB-SEM image of a shale sample to be processed.

[0035] The technicians collected images of shale samples through FIB-SEM, and the electronic equipment obtained the unprocessed FIB-SEM images sent by FIB-SEM. Since shale has strong heterogeneity, vertical scratches will be formed on the surface during the imaging process, so there is strip noise in the unprocessed FIB-SEM images.

[0036] In a possible implementation, each rock sample may correspond to multiple (e.g., 1000) FIB-SEM images to be processed, and the electronic device performs image processing on each acquired image.

[0037] Step 102, performing horizontal edge preserving filtering on the FIB-SEM image to be processed to obtain a horizontal filtering result.

[0038] The electronic device filters the processed FIB-SEM image through an edge-preserving filter algorithm to eliminate horizontal high-frequency signals. The edge-preserving filter algorithm blurs the image through convolution processing without damaging the edges of the image. The output after filtering can retain the overall outline of the image.

[0039] In one possible implementation, the electronic device may set a row window and use an edge-preserving filtering algorithm to smooth the pixels in each row window in turn, thereby ensuring that the edge of each row window is not destroyed, thereby achieving decomposition to obtain a smooth part and a horizontal high-frequency signal while ensuring that the overall image is not destroyed in the horizontal direction.

[0040] Step 103, decomposing and removing vertical strip noise based on the horizontal filtering result, and obtaining a target FIB-SEM image of the rock sample.

[0041] After performing the horizontal edge preservation filtering process, the vertical strip noise decomposition is performed to ensure that the vertical noise decomposition process does not destroy the horizontal texture of the image. In a possible implementation, the electronic device uses a guided filtering algorithm to extract the strip noise from the horizontal filtering result, specifically, the strip noise is extracted from the high-frequency signal part obtained by the horizontal filtering. After extracting and removing the strip noise, the target FIB-SEM image of the rock sample is obtained.

[0042] In summary, the present application provides a method for processing shale FIB-SEM images, which successively performs horizontal edge preservation filtering and vertical strip noise decomposition on the shale FIB-SEM images, decomposes and eliminates the vertical strip noise while ensuring the stability of the horizontal edges, and obtains an improved shale FIB-SEM image. For shale FIB-SEM images with a large amount of strip noise, the method can solve the negative impact of stripe defects formed during the imaging process on the extraction of pore structures, and effectively improve the accuracy of fine characterization of shale gas reservoirs.

[0043] Embodiment 2

[0044] Please refer to Figure 2 , which shows a flow chart of a method for processing shale FIB-SEM images provided by another exemplary embodiment of the present application. The method comprises the following steps:

[0045] Step 201, obtaining a FIB-SEM image of a shale sample to be processed.

[0046] The specific implementation of step 201 can refer to the above step 101, and the embodiment of the present application will not be repeated here.

[0047] Step 202 , performing horizontal edge preserving filtering on the FIB-SEM image to be processed, and decomposing the FIB-SEM image to be processed into a smoothed image and a horizontal high-frequency component.

[0048] The horizontal high-frequency component is composed of strip noise signals, image textures and other noise signals.

[0049] In a possible implementation, the FIB-SEM image is expressed as follows:

[0050] υ i =u i +s i +t i

[0051] Where i is the pixel index, υ iis the original FIB-SEM image, u i is the smoothed image, s i is the strip noise, t i is image texture and other forms of noise.

[0052] First, the electronic device smoothes the pixels of the FIB-SEM image to be processed by an edge-preserving filtering algorithm, and splits the original FIB-SEM image to be processed into a smoothed image and a horizontal high-frequency component, as shown below:

[0053] υ i =u i +n i

[0054] Among them, n i It is the horizontal high-frequency component, which is composed of strip noise, image texture and other image noise. The expression is as follows:

[0055] n i =s i +t i

[0056] Optionally, the electronic device uses one-dimensional line-guided filtering to perform horizontal edge preserving filtering processing, and step 202 specifically includes the following steps:

[0057] The FIB-SEM image to be processed is used as the input image and the guide image, and a one-dimensional row guided filtering algorithm is used to smooth the FIB-SEM to obtain a smoothed image and a horizontal high-frequency component.

[0058] The electronic device uses a one-dimensional line-guided filtering to eliminate the horizontal high-frequency signal. In this process, the original image υ i is regarded as the input image and the guide image. Then the pixels are smoothed through a row window of size 1×w to ensure that the horizontal structure of each row is not destroyed, so as to extract the high-frequency components while maintaining the horizontal structure of the complete image.

[0059] Step 203, decomposing and removing vertical strip noise based on the smoothed image and horizontal high-frequency components to obtain a target FIB-SEM image of the rock sample.

[0060] In a possible implementation, the smooth part and the strip noise defining a single column have the following relationship:

[0061]

[0062] where w k is the column window centered at pixel k, w k The height of is h; are linear coefficients and in the local column window w k Keep constant in.

[0063] That is, there is a linear relationship with a constant coefficient between the stripe noise and the pixels of the smoothed image in the same column window. The electronic device can extract the stripe noise by calculating its linear function.

[0064] Optionally, the electronic device decomposes the vertical strip noise by one-dimensional column-guided filtering, and step 203 specifically includes the following steps:

[0065] Step 203a, using the horizontal high-frequency component as the input image and the smoothed image as the guide image, a linear coefficient is determined by one-dimensional column guided filtering, where the linear coefficient is the coefficient corresponding to the linear relationship between the strip noise and the smoothed image.

[0066] Step 203b, extracting stripe noise from the horizontal high-frequency component based on a linear relationship and removing the stripe noise to obtain a target FIB-SEM image.

[0067] Among them, the linear relationship expression includes:

[0068]

[0069] Among them, si is strip noise, u i To smooth the image, is the linear coefficient and i is the pixel index.

[0070] The calculation formula of the linear coefficient includes:

[0071]

[0072]

[0073] Among them, I i To guide the image, p i is the input image, For the input image p i In the column window w k The average value of pixels in, |w| is the column window w k The number of pixels in μ k and They are respectively the guide images I i In the column window w k The pixel mean and pixel variance in .

[0074] Embodiment 3

[0075] Please refer to Figure 3 , which shows a flow chart of a method for processing shale FIB-SEM images provided by another exemplary embodiment of the present application. The method comprises the following steps:

[0076] Step 301, obtaining a FIB-SEM image of a shale sample to be processed.

[0077] Among them, the FIB-SEM image to be processed is obtained by mechanically polishing and argon ion polishing the rock sample of preset volume and shape, drying it at a preset temperature for a preset time, and then applying carbon and performing FIB-SEM imaging.

[0078] Indicatively, the technician has a size of about 1 cm 3 After mechanical polishing of the cubic sample to form a relatively flat surface, it was placed in an argon ion polishing device for polishing. The sample was then dried at 65°C for 12 hours and coated with carbon for FIB-SEM imaging. Figure 4 and Figure 5 A schematic diagram of a FIB-SEM image to be processed and the effect of directly using the FIB-SEM image to be processed for threshold segmentation are shown respectively. It can be seen that there are a lot of strip noises in both when no filtering operation is performed.

[0079] The specific implementation of step 301 can refer to the above step 101, and the embodiment of the present application will not be repeated here.

[0080] Step 302: Perform horizontal edge preserving filtering on the FIB-SEM image to be processed to obtain a horizontal filtering result.

[0081] Step 303: Decompose and remove vertical strip noise based on the horizontal filtering result to obtain a target FIB-SEM image of the rock sample.

[0082] The specific implementation of step 302 to step 303 can refer to the above-mentioned step 102 to step 103, and the embodiment of the present application will not be repeated here.

[0083] Figure 6 The effect diagram of using the method of the embodiment of the present application to remove stripe noise from the processed FIB-SEM image and then perform threshold segmentation is shown. Figure 5 , a lot of vertical strip noise is obviously reduced.

[0084] Step 304 , based on a pixel template of a preset size, median filtering is performed on the target FIB-SEM image, and the pixel value of each target pixel in the target FIB-SEM image is replaced with the average value of the pixels around the target pixel.

[0085] In addition to stripe noise, there are other forms of noise in the original FIB-SEM image, such as salt and pepper noise. In the embodiment of the present application, after vertical stripe noise decomposition, the electronic device removes salt and pepper noise from the target FIB-SEM image through a median filtering algorithm.

[0086] Schematically, the electronic device calculates the average value of the 24 pixels around the target point based on a template with a size of 5×5 pixels, and then the pixel value of the target point is replaced by the average value. All pixels of the entire target FIB-SEM image are median filtered to remove salt and pepper noise to the greatest extent.

[0087] Figure 7 The figure shows the effect of a FIB-SEM image after one-dimensional guided filtering and median filtering and then threshold segmentation. Figure 8 The corresponding FIB-SEM images are shown.

[0088] In the embodiment of the present application, the target rock sample is first subjected to mechanical polishing and argon ion polishing, and a FIB-SEM three-dimensional imaging experiment is performed to obtain a series of FIB-SEM images to be processed, and then the above images are subjected to one-dimensional guided filtering, including two steps of horizontal edge retention filtering and vertical strip noise decomposition; then, based on the above data, the median filtering algorithm is used to remove the salt and pepper noise, thereby obtaining an improved target FIB-SEM image. For shale FIB-SEM images with a large amount of strip noise, the method provided in the embodiment of the present application has fast calculation efficiency and obvious improvement effect, which can effectively improve the accuracy of fine characterization of shale gas reservoirs.

[0089] Embodiment 4

[0090] Please refer to Fig. 9 , which shows a structural block diagram of a shale FIB-SEM image processing device provided by an exemplary embodiment of the present application, the device comprising:

[0091] An acquisition module 901 is used to acquire a focused ion beam scanning electron microscope (FIB-SEM) image of the shale sample to be processed;

[0092] A filtering module 902 is used to perform horizontal edge preserving filtering on the FIB-SEM image to be processed to obtain a horizontal filtering result;

[0093] The decomposition module 903 is used to decompose and remove vertical strip noise based on the horizontal filtering result to obtain a target FIB-SEM image of the rock sample.

[0094] Optionally, the filtering module 902 is further used for:

[0095] Performing horizontal edge-preserving filtering on the FIB-SEM image to be processed, decomposing the FIB-SEM image to be processed into a smooth image and a horizontal high-frequency component, wherein the horizontal high-frequency component is composed of a strip noise signal, an image texture, and other noise signals;

[0096] The decomposition module 903 is further used for:

[0097] Based on the smoothed image and the horizontal high-frequency component, vertical strip noise is decomposed and removed to obtain a target FIB-SEM image of the rock sample.

[0098] Optionally, the filtering module 902 is further used for:

[0099] The FIB-SEM image to be processed is used as an input image and a guide image, and a one-dimensional row guided filtering algorithm is used to smooth the FIB-SEM image to be processed to obtain the smoothed image and the horizontal high-frequency component.

[0100] Optionally, the decomposition module 903 is further used to:

[0101] Taking the horizontal high-frequency component as an input image and the smoothed image as a guide image, a linear coefficient is determined by one-dimensional column guided filtering, wherein the linear coefficient is a coefficient corresponding to a linear relationship between the strip noise and the smoothed image;

[0102] Based on the linear relationship, stripe noise is extracted from the horizontal high-frequency component and the stripe noise is removed to obtain the target FIB-SEM image.

[0103] Optionally, the linear relationship expression includes:

[0104]

[0105] Among them, si is strip noise, u i is the smoothed image, is the linear coefficient, i is the pixel index;

[0106] The calculation formula of the linear coefficient includes:

[0107]

[0108]

[0109] Among them, I i is the guide image, p i is the input image, For the input image p i In the column window w k The pixel average in |w| is the column window wk The number of pixels in μ k and are the guide images I i In the column window w k The pixel mean and pixel variance in .

[0110] Optionally, the FIB-SEM image to be processed is obtained by mechanically polishing and argon ion polishing a rock sample of preset volume and shape, drying it at a preset temperature for a preset time, and then coating it with carbon and performing FIB-SEM imaging.

[0111] Optionally, the device further comprises:

[0112] The processing module is used to perform median filtering on the target FIB-SEM image based on a pixel template of a preset size, and replace the pixel value of each target pixel point in the target FIB-SEM image with the average value of the pixel points around the target pixel point.

[0113] The present application provides a shale FIB-SEM image processing device, which performs horizontal edge retention filtering and vertical strip noise decomposition on the shale FIB-SEM image in succession, decomposes the vertical strip noise while ensuring the stability of the horizontal edge, and obtains an improved shale FIB-SEM image. For shale FIB-SEM images with a large amount of strip noise, the negative impact of stripe defects formed during the imaging process on pore structure extraction can be resolved, and the accuracy of fine characterization of shale gas reservoirs can be effectively improved.

[0114] Embodiment 5

[0115] An embodiment of the present application provides an electronic device; Fig.10 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Fig.10 As shown, the electronic device 1000 includes: a processor 1001, at least one communication bus 1002, a user interface 1003, at least one external communication interface 1004, and a memory 1005. The communication bus 1002 is configured to realize the connection and communication between these components. The user interface 1003 may include a display screen, and the external communication interface 1004 may include a standard wired interface and a wireless interface. The processor 1001 is configured to execute the program of the deep coalbed methane selection evaluation method stored in the memory to implement the steps in the method provided in the above embodiment.

[0116] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored. The computer program is loaded and executed by a processor to implement the method described in the above embodiment.

[0117] The embodiment of the present application also provides a computer program product, which runs on a processor of a computer device, so that the computer device executes the method described in the above embodiment. It should be noted here that the description of the above storage medium, electronic device, and remote control embodiment is similar to the description of the above method embodiment, and has similar beneficial effects as the method embodiment. For technical details not disclosed in the storage medium and device embodiments of the present application, please refer to the description of the method embodiment of the present application for understanding.

[0118] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0119] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, object or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, object or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, object or device including the element.

[0120] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0121] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0122] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be a separate unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0123] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read Only Memory), disks or optical disks, etc. Various media that can store program codes.

[0124] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a controller to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0125] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for processing shale FIB-SEM images, characterized in that: include: Obtaining FIB-SEM images of the shale samples to be processed; Performing horizontal edge-preserving filtering on the FIB-SEM image to be processed to obtain a horizontal filtering result; Based on the horizontal filtering result, vertical strip noise is decomposed and removed to obtain a target FIB-SEM image of the rock sample.

2. The method according to claim 1, characterized in that: The step of performing horizontal edge-preserving filtering on the FIB-SEM image to be processed to obtain a horizontal filtering result includes: Performing horizontal edge-preserving filtering on the FIB-SEM image to be processed, decomposing the FIB-SEM image to be processed into a smooth image and a horizontal high-frequency component, wherein the horizontal high-frequency component is composed of a strip noise signal, an image texture, and other noise signals; Decomposing and removing vertical strip noise based on the horizontal filtering result to obtain a target FIB-SEM image of the rock sample includes: Based on the smoothed image and the horizontal high-frequency component, vertical strip noise is decomposed and removed to obtain a target FIB-SEM image of the rock sample.

3. The method according to claim 2, characterized in that The step of performing horizontal edge-preserving filtering on the FIB-SEM image to be processed to decompose the FIB-SEM image to be processed into a smooth image and a horizontal high-frequency component includes: The FIB-SEM image to be processed is used as an input image and a guide image, and a one-dimensional row guided filtering algorithm is used to smooth the FIB-SEM image to be processed to obtain the smoothed image and the horizontal high-frequency component.

4. The method according to claim 2, characterized in that: The vertical strip noise decomposition is performed based on the smoothed image and the horizontal high-frequency component to obtain a target FIB-SEM image of the rock sample, including: Taking the horizontal high-frequency component as an input image and the smoothed image as a guide image, a linear coefficient is determined by one-dimensional column guided filtering, wherein the linear coefficient is a coefficient corresponding to a linear relationship between the strip noise and the smoothed image; Based on the linear relationship, stripe noise is extracted from the horizontal high-frequency component and the stripe noise is removed to obtain the target FIB-SEM image.

5. The method according to claim 4, characterized in that The linear relationship expression includes: Among them, si is strip noise, u i is the smoothed image, is the linear coefficient, i is the pixel index; The calculation formula of the linear coefficient includes: Among them, I i is the guide image, p i is the input image, For the input image p i In the column window w k The pixel average value in |w| is the column window w k The number of pixels in μ k and are the guide images I i In the column window w k The pixel mean and pixel variance in .

6. The method according to any one of claims 1 to 5, characterized in that: The FIB-SEM image to be processed is obtained by mechanically polishing and argon ion polishing a rock sample of a preset volume and shape, drying it at a preset temperature for a preset time, and then applying carbon and performing FIB-SEM imaging.

7. The method according to any one of claims 1 to 5, characterized in that: After decomposing and removing the vertical strip noise based on the horizontal filtering result to obtain the target FIB-SEM image of the rock sample, the method further includes: Based on a pixel template of a preset size, the target FIB-SEM image is subjected to median filtering, and the pixel value of each target pixel in the target FIB-SEM image is replaced by the average value of the pixels around the target pixel.

8. A shale FIB-SEM image processing device, characterized in that: include: An acquisition module, used for acquiring a focused ion beam scanning electron microscope FIB-SEM image of the shale rock sample to be processed; A filtering module, used for performing horizontal edge-preserving filtering on the FIB-SEM image to be processed to obtain a horizontal filtering result; A decomposition module is used to decompose and remove vertical strip noise based on the horizontal filtering result to obtain a target FIB-SEM image of the rock sample.

9. An electronic device, characterized in that: The method comprises a memory and a processor; a computer program is stored in the memory, and when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored, and the computer program is loaded and executed by a processor to implement the method according to any one of claims 1 to 7.

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