Methods, systems, electronic devices and media for image restoration under high temperature scanning electron microscope
By adjusting the ratio of high-frequency and low-frequency components in a scanning electron microscope (SEM) and combining it with the thermionic signal voltage value, a Butterworth filtering algorithm was used to repair SEM images at high temperatures. This solved the problem of poor signal-to-noise ratio caused by thermionic interference and achieved clear imaging at high temperatures.
Patent Information
- Application Number
- CN202210997434.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-08-19
- Publication Date
- 2025-12-02
- Estimated Expiration
- 2042-08-19
Smart Images
Figure CN115358946B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of image restoration technology, and in particular to a method, system, electronic device, and medium for restoring scanning electron microscope images at high temperatures. Background Technology
[0002] The metallic materials used in aircraft engines operate at temperatures of hundreds or even thousands of degrees Celsius. In order to study the various properties of these materials, they need to be heated in the vacuum chamber of a scanning electron microscope (hereinafter referred to as a scanning electron microscope) to observe the changes in their microstructure.
[0003] In existing vacuum chamber heating experiments using scanning electron microscopes (SEMs), when the temperature reaches above 800 degrees Celsius, the electron movement within the metallic sample becomes intense, and electrons are more easily excited out. This causes some of the secondary electron signals in the imaging to be masked by the thermionic signals released by the metallic sample. Consequently, the SEM image may exhibit localized blurring, or even display snowflake-like noise or a white haze-like appearance, resulting in a very poor signal-to-noise ratio. Clearly, high-temperature imaging with SEM remains a significant challenge in the study of high-temperature metallic materials. Summary of the Invention
[0004] The purpose of this invention is to provide a method, system, device, and medium for restoring scanning electron microscope (SEM) images at high temperatures, which can restore SEM images at high temperatures and improve the signal-to-noise ratio of SEM images.
[0005] To achieve the above objectives, the present invention provides the following solution:
[0006] In a first aspect, the present invention provides a method for restoring scanning electron microscope images at high temperature, comprising:
[0007] Obtain scanning electron microscope (SEM) images of the sample to be repaired; the SEM images are SEM images of metallic material samples.
[0008] Extract the high-frequency and low-frequency components of the scanning electron microscope sample image, and determine the sharpness of the scanning electron microscope sample image based on the high-frequency and low-frequency components.
[0009] When the resolution is lower than a set threshold, the thermionic signal voltage value corresponding to the scanning electron microscope sample image is obtained; the thermionic signal voltage value is the release voltage value generated when thermionic electrons are released.
[0010] Based on the target relationship and the thermionic signal voltage value corresponding to the scanning electron microscope sample image, the multiplier by which the high-frequency component increases and the multiplier by which the low-frequency component decreases in the filtering algorithm are determined; the target relationship is the relationship between the thermionic signal voltage value and the image signal-to-noise ratio.
[0011] The scanning electron microscope sample image is repaired based on the multiple by which the high-frequency components are increased and the multiple by which the low-frequency components are reduced in the filtering algorithm.
[0012] Optionally, the method for determining the target relationship is as follows:
[0013] Scanning electron microscope (SEM) images of samples at high temperatures, the release voltage values generated when the thermionic electrons are released corresponding to the SEM images, and the acquisition time of the release voltage values are all collected using a scanning electron microscope (SEM). The SEM images are SEM images of experimental metallic material samples.
[0014] The scanning electron microscope (SEM) was used to acquire images of the SEM sample at low temperature, the release voltage value generated when the thermionic electrons were released corresponding to the SEM sample image, and the acquisition time of the release voltage value.
[0015] Using the acquisition time of the release voltage value, the scanning electron microscope sample image is divided to obtain the thermionic imaging region and the secondary electron imaging region;
[0016] The image signal-to-noise ratio of the scanning electron microscope sample image is determined based on the thermionic imaging region and the secondary electron imaging region.
[0017] Based on the release voltage value and image signal-to-noise ratio generated when the hot electrons are released from the corresponding scanning electron microscope sample image, a target relationship is constructed.
[0018] Optionally, the extraction of the high-frequency and low-frequency components of the scanning electron microscope sample image specifically includes:
[0019] Take the logarithm of the expression for the scanning electron microscope sample image;
[0020] Perform a Fourier transform on the expression of the SEM sample image after taking the logarithm to obtain the Fourier transformed SEM sample image data.
[0021] Based on the Fourier transform of the scanning electron microscope (SEM) sample image data, determine the high-frequency and low-frequency components of the SEM sample image.
[0022] Optionally, determining the sharpness of the scanning electron microscope sample image based on the high-frequency component and the low-frequency component specifically includes:
[0023] The resolution of the scanning electron microscope sample image is obtained by calculating the ratio between the high-frequency component and the low-frequency component.
[0024] Optionally, the step of acquiring the thermionic signal voltage value corresponding to the scanning electron microscope sample image when the sharpness is lower than a set threshold specifically includes:
[0025] Determine whether the resolution is greater than a set threshold;
[0026] If so, save the scanning electron microscope sample image;
[0027] If not, then obtain the thermionic signal voltage value corresponding to the scanning electron microscope sample image.
[0028] Optionally, determining the factor by which high-frequency components are increased and low-frequency components are decreased in the filtering algorithm based on the target relationship and the thermionic signal voltage value corresponding to the scanning electron microscope sample image specifically includes:
[0029] Based on the target relationship and the thermionic signal voltage value corresponding to the scanning electron microscope sample image, the image signal-to-noise ratio of the scanning electron microscope sample image is determined;
[0030] Based on the image signal-to-noise ratio, determine the factor by which the high-frequency components increase and the factor by which the low-frequency components decrease in the Butterworth filtering algorithm.
[0031] Optionally, the step of repairing the scanning electron microscope sample image based on the multiple by which the high-frequency components increase and the multiple by which the low-frequency components decrease in the filtering algorithm specifically includes:
[0032] The scanning electron microscope sample image is repaired based on the multiple by which the high-frequency components are increased and the multiple by which the low-frequency components are decreased in the Butterworth filtering algorithm.
[0033] In a second aspect, the present invention provides a scanning electron microscope image restoration system at high temperature, comprising:
[0034] A scanning electron microscope (SEM) sample image acquisition module is used to acquire SEM images of the sample to be repaired; the SEM sample images are SEM images of metallic material samples.
[0035] A sharpness calculation module is used to extract the high-frequency and low-frequency components of the scanning electron microscope sample image, and determine the sharpness of the scanning electron microscope sample image based on the high-frequency and low-frequency components.
[0036] The thermionic signal voltage value acquisition module is used to acquire the thermionic signal voltage value corresponding to the scanning electron microscope sample image when the resolution is lower than a set threshold; the thermionic signal voltage value is the release voltage value generated when thermionic electrons are released;
[0037] The high-frequency and low-frequency component adjustment factor determination module is used to determine the factor by which the high-frequency component is increased and the factor by which the low-frequency component is decreased in the filtering algorithm based on the target relationship and the thermionic signal voltage value corresponding to the scanning electron microscope sample image; the target relationship is the relationship between the thermionic signal voltage value and the image signal-to-noise ratio;
[0038] The image restoration module is used to restore the scanning electron microscope sample image based on the multiple by which the high-frequency components are increased and the multiple by which the low-frequency components are decreased in the filtering algorithm.
[0039] Thirdly, the present invention provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the high-temperature scanning electron microscope image restoration method according to the first aspect.
[0040] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the high-temperature scanning electron microscope image restoration method as described in the first aspect.
[0041] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:
[0042] In existing scanning electron microscope (SEM) vacuum chamber heating experiments, when the temperature of metallic samples rises above 800°C, the SEM images exhibit localized blurring, or even display snowflake-like noise or a white haze-like appearance across the entire image. Therefore, this invention provides a method, system, device, and medium for SEM image restoration at high temperatures. This invention combines image monitoring and filtering algorithms to adjust pixels in locally blurred SEM images, improving the signal-to-noise ratio and enabling normal imaging when observing high-temperature metallic samples. Attached Figure Description
[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0044] Figure 1 This is a schematic flowchart of the high-temperature scanning electron microscope image restoration method disclosed in Embodiment 1 of the present invention;
[0045] Figure 2 This is a schematic diagram of the high-temperature scanning electron microscope image restoration system disclosed in Embodiment 2 of the present invention. Detailed Implementation
[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0048] When using scanning electron microscopy (SEM) to observe metallic samples, secondary electrons are typically used for imaging. However, in in-situ high-temperature observations of metallic samples, the secondary electron signal is affected by the thermionic electrons released from the sample. The electron signal excited by the electron beam striking the sample exhibits periodic variations, sometimes more, sometimes less. When electrons on the sample surface are instantaneously excited, this manifests in the image as overly bright pixels, snow-like noise, or a hazy appearance, significantly reducing the signal-to-noise ratio of the SEM image. Furthermore, due to the increased temperature of the heated sample, the overall SEM image becomes washed out and blurry, making it impossible to discern detailed information about the metallic sample. To address the above problems, this invention provides a method and system for restoring scanning electron microscope (SEM) images at high temperatures. Based on the principle of secondary electron imaging, this invention improves the imaging system of the SEM by using an embedded system to acquire imaging signals within the vacuum chamber of the SEM and combining this with a sharpness algorithm to determine the signal-to-noise ratio of the SEM image. By suppressing thermionic signals and enhancing secondary electron signals, the invention achieves the goal of clearly viewing detailed information of metallic material samples at higher temperatures.
[0049] Example 1
[0050] At high temperatures, the obtained scanning electron microscope (SEM) image is denoted as I(i,j), which can be divided into a secondary electron signal component l(i,j) and a thermionic signal component r(i,j) according to its electronic signal. The SEM image expression is as follows:
[0051] I(i,j)=r(i,j)·l(i,j) (1);
[0052] In this image, the thermionic information component r(l,j) and the secondary electron signal component l(i,i) are a product, where i and j are the row and column numbers of the image, respectively, and (i,j) is the coordinate of a pixel. The thermionic information component r(i,j) mainly consists of thermionic electrons released by the metallic sample at high temperatures, corresponding to the low-frequency component of the SEM image. The secondary electron signal component l(i,j) is the secondary electron excited when the scanning electron beam strikes the metallic sample, reflecting the surface morphology and other characteristic information of the sample, corresponding to the high-frequency component of the SEM image. When the temperature of the metallic sample increases, the released thermionic signal partially masks the secondary electron signal, resulting in an overall white and blurry SEM image that fails to reveal detailed information about the metallic sample.
[0053] To facilitate calculation, taking the logarithm of both sides of the scanning electron microscope image expression yields the logarithmic scanning electron microscope image expression shown in formula (2).
[0054] f(i,j)=lnI(i,j)=lnr(i,j)+lnl(i,j) (2);
[0055] The Fourier transform of formula (2) is performed as follows:
[0056]
[0057] Expanding the above equation, we get:
[0058]
[0059] Where m and n represent the number of iterations.
[0060] For the k-th order Butterworth filter function, its high-pass filter function is:
[0061]
[0062] The low-pass filter function is:
[0063]
[0064] After applying Butterworth filtering to formula (4), the formula after Butterworth filtering is:
[0065] S(i,j)=H(i,j)F(i,j)=H h (i,j)F(i,j)+H l (i,j)F(i,j) (7);
[0066] Where, r h and r lThese represent the factors that increase the high-frequency components and decrease the low-frequency components, respectively. D0 represents the cutoff frequency, D is the distance from point (i,j) to the origin of the filter, and the constant c controls the sharpening effect of the filter function on the scanning electron microscope image. By adjusting r... h and r l The value of r is used to adjust the ratio of low-frequency to high-frequency components in a scanning electron microscope image. For example, selecting r... h >1, r l <1 can achieve the purpose of suppressing low-frequency components of scanning electron microscope (SEM) images and enhancing high-frequency components of SEM images, thereby adjusting the clarity of SEM images.
[0067] Extraction of high-frequency and low-frequency components from scanning electron microscope images:
[0068]
[0069]
[0070] The ratio of the extracted high-frequency components to the low-frequency components is used to obtain the sharpness discrimination function, as follows:
[0071]
[0072] In the above formula, generally speaking, the larger μ is, the higher the sharpness of the scanning electron microscope (SEM) image. This formula is used to determine the sharpness of the SEM image in real time during high-temperature imaging. When the sharpness is determined to be low, the image processing method mentioned in the previous steps is used, and r is adjusted in conjunction with the relationship between the acquired thermionic signal voltage value and the image signal-to-noise ratio. h and r l The value of is used to perform Butterworth filtering on scanning electron microscope images to obtain images with higher clarity.
[0073] like Figure 1 As shown in the figure, the present invention provides a method for restoring scanning electron microscope images at high temperature, which includes the following steps.
[0074] Step 100: Obtain the scanning electron microscope (SEM) image of the sample to be repaired; the SEM image is a scanning electron microscope image of a metallic material sample;
[0075] Step 200: Extract the high-frequency and low-frequency components of the scanning electron microscope sample image, and determine the sharpness of the scanning electron microscope sample image based on the high-frequency and low-frequency components;
[0076] Step 300: When the resolution is lower than a set threshold, acquire the thermionic signal voltage value corresponding to the scanning electron microscope sample image; the thermionic signal voltage value is the release voltage value generated when thermionic electrons are released.
[0077] Step 400: Based on the target relationship and the thermionic signal voltage value corresponding to the scanning electron microscope sample image, determine the multiple by which the high-frequency components are increased and the multiple by which the low-frequency components are decreased in the filtering algorithm; the target relationship is the relationship between the thermionic signal voltage value and the image signal-to-noise ratio;
[0078] Step 500: Based on the multiple by which the high-frequency components increase and the multiple by which the low-frequency components decrease in the filtering algorithm, repair the scanning electron microscope sample image.
[0079] Furthermore, the method provided in this embodiment of the invention further includes: determining the target relationship.
[0080] The method for determining the target relationship is as follows:
[0081] First, a scanning electron microscope (SEM) is used to acquire SEM sample images at high temperatures, the release voltage value generated when the thermionic electrons are released corresponding to the SEM sample images, and the acquisition time of the release voltage value; the SEM sample images are SEM images of experimental metallic material samples.
[0082] Second, a scanning electron microscope (SEM) is used to acquire images of a SEM sample at low temperature, the release voltage value generated when the thermionic electrons corresponding to the SEM sample image are released, and the acquisition time of the release voltage value.
[0083] Third, using the acquisition time of the release voltage value, the scanning electron microscope sample image is divided to obtain the thermionic imaging region and the secondary electron imaging region.
[0084] Fourth, the image signal-to-noise ratio of the scanning electron microscope sample image is determined based on the thermionic imaging region and the secondary electron imaging region.
[0085] Fifth, based on the release voltage value and image signal-to-noise ratio generated when the hot electrons are released corresponding to the scanning electron microscope sample image, a target relationship is constructed.
[0086] One example is: the process of determining the target relationship is as follows:
[0087] Step 1: Set the heating temperature of the vacuum chamber heater of the scanning electron microscope to be greater than 700 degrees Celsius (e.g., 800 degrees Celsius, 1100 degrees Celsius, 1300 degrees Celsius, 1500 degrees Celsius). Place the experimental metal material sample in the vacuum chamber of the scanning electron microscope and heat it. When the actual temperature reaches the set temperature, use the scanning electron microscope to scan it from top to bottom.
[0088] Step 2: Acquire scanning electron microscope (SEM) images of the experimental metallic material samples at high temperatures (hereinafter referred to as SEM sample images), and save the SEM sample images containing thermionic signal interference. The numerous white noise points in the SEM sample images are interference signals generated during thermionic emission. These noise points cause some pixels in the SEM sample images to be extremely large or small, resulting in excessively bright or dark noise interference that affects the image quality. The more noise, the worse the overall contrast of the SEM sample image; correspondingly, more thermionic electrons are released, and the greater the voltage generated during release.
[0089] Step 3: Use the high-speed acquisition coil of the embedded system to acquire the release voltage value generated when the hot electrons are released in the scanning electron microscope sample image with local or overall noise, and record the time. Use a high-speed data acquisition card to acquire imaging data, and save the acquired image data and imaging signal voltage value to the computer via the computer serial port. That is, the embedded system is used inside the vacuum cavity of the scanning electron microscope to collect the electronic signals that appear when the scanning electron microscope sample image is captured at high speed using an induction coil, and the acquired disturbance data packets are transmitted to the external computer for storage.
[0090] Step 4: Compare the imaging signal and voltage value of the scanning electron microscope sample image obtained in Step 3 with the imaging signal and voltage value of the scanning electron microscope sample image at low temperature, and analyze the voltage data to obtain the relationship between the thermionic signal voltage value and the image signal-to-noise ratio.
[0091] Step 5: To understand the relationship between the thermionic signal voltage value and the image signal-to-noise ratio in Step 4, an algorithm program was written in C language using VC6.0 software. This program was then used to correct the scanning electron microscope sample image.
[0092] Specifically, the location of the interference region on the scanning electron microscope (SEM) sample image is determined by the time of the measured interference signal generation, thereby identifying the high-frequency and low-frequency signal regions corresponding to the SEM sample image.
[0093] In step 200, extracting the high-frequency and low-frequency components of the scanning electron microscope sample image specifically includes:
[0094] First, the expression of the scanning electron microscope (SEM) sample image is logarithmically transformed. Then, a Fourier transform is performed on the logarithmically transformed expression of the SEM sample image to obtain the Fourier transformed SEM sample image data. Finally, based on the Fourier transformed SEM sample image data, the high-frequency and low-frequency components of the SEM sample image are determined. The formulas for the logarithm operation and the Fourier transform are shown in formulas (1)-(4).
[0095] In step 200, determining the sharpness of the scanning electron microscope sample image based on the high-frequency component and the low-frequency component specifically includes:
[0096] The image sharpness discrimination function described in formula (10) is used to determine the sharpness of the scanning electron microscope sample image, that is, to calculate the ratio of the high frequency component to the low frequency component to obtain the sharpness of the scanning electron microscope sample image.
[0097] As a preferred embodiment of step 300, it specifically includes:
[0098] Determine whether the resolution is greater than a set threshold; if yes, save the scanning electron microscope (SEM) sample image; if no, obtain the thermionic signal voltage value corresponding to the SEM sample image.
[0099] As a preferred embodiment of step 400, it specifically includes:
[0100] Based on the target relationship and the thermionic signal voltage value corresponding to the scanning electron microscope sample image, the image signal-to-noise ratio of the scanning electron microscope sample image is determined; based on the image signal-to-noise ratio, the factor by which the high-frequency components are increased and the factor by which the low-frequency components are reduced in the Butterworth filtering algorithm are determined.
[0101] Further, step 500 specifically includes:
[0102] The scanning electron microscope sample image is repaired based on the multiple by which the high-frequency components are increased and the multiple by which the low-frequency components are decreased in the Butterworth filtering algorithm.
[0103] One example is:
[0104] Step (1): Take the logarithm of the expression of the scanning electron microscope sample image.
[0105] Step (2): Perform a Fourier transform on the expression of the SEM sample image after the logarithmic operation to obtain the Fourier transformed SEM sample image data.
[0106] Step (3): Determine the high-frequency and low-frequency components of the scanning electron microscope sample image based on the Fourier transformed scanning electron microscope sample image data.
[0107] Step (4): Based on the multiple by which the high-frequency components are increased and the multiple by which the low-frequency components are decreased in the Butterworth filtering algorithm, the high-frequency components and low-frequency components of the scanning electron microscope sample image are adjusted to obtain the adjusted scanning electron microscope sample image.
[0108] Step (5): Perform an inverse Fourier transform on the adjusted scanning electron microscope sample image to obtain a scanning electron microscope image in positive space.
[0109] Example 2
[0110] In order to implement the method corresponding to Embodiment 1 above and achieve the corresponding functions and technical effects, a scanning electron microscope image restoration system at high temperature is provided below.
[0111] like Figure 2 As shown in the figure, an embodiment of the present invention provides a scanning electron microscope image restoration system at high temperature, comprising:
[0112] The scanning electron microscope (SEM) sample image acquisition module 1 is used to acquire the SEM sample image to be repaired; the SEM sample image is a SEM image of a metallic material sample.
[0113] The sharpness calculation module 2 is used to extract the high-frequency and low-frequency components of the scanning electron microscope sample image, and determine the sharpness of the scanning electron microscope sample image based on the high-frequency and low-frequency components.
[0114] The thermionic signal voltage value acquisition module 3 is used to acquire the thermionic signal voltage value corresponding to the scanning electron microscope sample image when the clarity is lower than a set threshold; the thermionic signal voltage value is the release voltage value generated when thermionic electrons are released.
[0115] The high and low frequency component adjustment factor determination module 4 is used to determine the factor by which the high frequency component is increased and the factor by which the low frequency component is decreased in the filtering algorithm based on the target relationship and the thermionic signal voltage value corresponding to the scanning electron microscope sample image; the target relationship is the relationship between the thermionic signal voltage value and the image signal-to-noise ratio.
[0116] Image restoration module 5 is used to restore the scanning electron microscope sample image based on the multiple by which the high-frequency components are increased and the multiple by which the low-frequency components are decreased in the filtering algorithm.
[0117] Example 3
[0118] This invention provides an electronic device including a memory and a processor. The memory stores a computer program, and the processor runs the computer program to enable the electronic device to perform the high-temperature scanning electron microscope image restoration method of Embodiment 1.
[0119] Alternatively, the aforementioned electronic device may be a server.
[0120] In addition, this embodiment of the invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the high-temperature scanning electron microscope image restoration method of Embodiment 1.
[0121] In existing scanning electron microscope (SEM) vacuum chamber heating experiments, when the temperature reaches 800 degrees Celsius, the electron movement within the metallic sample becomes more intense, and electrons are more easily excited out. This causes some of the secondary electron signals in the imaging to be masked by the thermionic signals released by the metallic sample. Consequently, the SEM image exhibits corresponding local blurring, or even snowflake-like noise or a white haze-like appearance, resulting in a very poor signal-to-noise ratio. Therefore, this invention employs an image sharpness discrimination function and uses point-to-point detection of the voltage generated by thermionic electrons. By combining these two methods, the high-frequency and low-frequency information of the image is modified in real time, enabling stable and clear imaging even when observing high-temperature metallic samples.
[0122] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.
[0123] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.
Claims
1. A method for restoring scanning electron microscope images at high temperature, characterized in that, include: Obtain scanning electron microscope images of the sample to be repaired; The scanning electron microscope (SEM) sample image is a scanning electron microscope image of a metallic material sample; The high-frequency and low-frequency components of the scanning electron microscope (SEM) sample image are extracted, and the sharpness of the SEM sample image is determined based on the high-frequency and low-frequency components. Specifically, extracting the high-frequency and low-frequency components of the SEM sample image includes: taking the logarithm of the expression for the SEM sample image: f(i,j) = lnI(i,j) = ln r(i,j) + lnl(i,j), where I(i,j) represents the SEM sample image, k(i,j) represents the secondary electron signal component, r(i,j) represents the thermionic signal component, and (i,j) represents the position coordinates of a pixel; and performing a Fourier transform on the logarithm-transformed expression for the SEM sample image to obtain the Fourier-transformed SEM sample image data. M and N represent the dimensions of the scanning electron microscope (SEM) sample image; based on the SEM sample image data after Fourier transform, the high-frequency and low-frequency components of the SEM sample image are determined. When the resolution is lower than a set threshold, the thermionic signal voltage value corresponding to the scanning electron microscope sample image is obtained; the thermionic signal voltage value is the release voltage value generated when thermionic electrons are released. Based on the target relationship and the thermionic signal voltage value corresponding to the scanning electron microscope (SEM) sample image, the factors by which high-frequency components increase and low-frequency components decrease in the filtering algorithm are determined. Specifically, this includes: determining the image signal-to-noise ratio (SNR) of the SEM sample image based on the target relationship and the thermionic signal voltage value corresponding to the SEM sample image; and determining the factors by which high-frequency components increase and low-frequency components decrease in the Butterworth filtering algorithm based on the image SNR. The target relationship is the relationship between the thermionic signal voltage value and the image SNR. The scanning electron microscope (SEM) sample image is repaired based on the factors that increase the high-frequency components and decrease the low-frequency components in the filtering algorithm. Specifically, this includes repairing the SEM sample image based on the factors that increase the high-frequency components and decrease the low-frequency components in the Butterworth filtering algorithm. The specific calculation formula is as follows: S(i,j)=H h (i,j)F(i,j)+H l (i,j)F(i,j); In the formula, H h (i,j) represents the high-pass filter function in the k-th order Butterworth filter function, H l (i,j) represents the low-pass filter function in the k-th order Butterworth filter function, r h and r l These represent the factors that increase the high-frequency components and decrease the low-frequency components, respectively. D0 represents the cutoff frequency, D(i,j) is the distance from point (i,j) to the origin of the filter, and the constant c is used to control the degree of sharpening of the scanning electron microscope image by the filter function.
2. The method for restoring scanning electron microscope images at high temperature according to claim 1, characterized in that, The method for determining the target relationship is as follows: The scanning electron microscope (SEM) was used to acquire images of the sample at high temperature, the release voltage value generated when the thermionic electrons were released corresponding to the SEM sample images, and the acquisition time of the release voltage value. The scanning electron microscope (SEM) sample images are SEM images of experimental metallic material samples; The scanning electron microscope (SEM) was used to acquire images of the sample at low temperature, the release voltage value generated when the thermionic electrons were released corresponding to the SEM sample images, and the acquisition time of the release voltage value. Using the acquisition time of the release voltage value, the scanning electron microscope sample image is divided to obtain the hot electron imaging region and the secondary electron imaging region; The image signal-to-noise ratio of the scanning electron microscope sample image is determined based on the thermionic imaging region and the secondary electron imaging region. Based on the release voltage value and image signal-to-noise ratio generated when the thermionic electrons are released from the scanning electron microscope sample image, a target relationship is constructed.
3. The method for restoring scanning electron microscope images at high temperature according to claim 1, characterized in that, Determining the sharpness of the scanning electron microscope sample image based on the high-frequency component and the low-frequency component specifically includes: The resolution of the scanning electron microscope sample image is obtained by calculating the ratio between the high-frequency component and the low-frequency component.
4. The method for restoring scanning electron microscope images at high temperature according to claim 1, characterized in that, When the resolution is lower than a set threshold, the process of acquiring the thermionic signal voltage value corresponding to the scanning electron microscope sample image specifically includes: Determine whether the resolution is greater than a set threshold; If so, save the scanning electron microscope sample image; If not, then obtain the thermionic signal voltage value corresponding to the scanning electron microscope sample image.
5. A scanning electron microscope image restoration system at high temperature, characterized in that, include: The scanning electron microscope (SEM) sample image acquisition module is used to acquire images of the SEM sample to be repaired. The scanning electron microscope (SEM) sample image is a scanning electron microscope image of a metallic material sample; The sharpness calculation module is used to extract the high-frequency and low-frequency components of the scanning electron microscope (SEM) sample image and determine the sharpness of the SEM sample image based on the high-frequency and low-frequency components. Specifically, extracting the high-frequency and low-frequency components of the SEM sample image includes: taking the logarithm of the expression of the SEM sample image: f(i,j)=lnI(i,j)=ln r(i,j)+lnl(i,j), where I(i,j) represents the SEM sample image, l(i,j) represents the secondary electron signal component, r(i,j) represents the thermionic signal component, and (i,j) represents the position coordinates of a pixel; and performing a Fourier transform on the logarithm-transformed expression of the SEM sample image to obtain the Fourier-transformed SEM sample image data. M and N represent the dimensions of the scanning electron microscope (SEM) sample image; based on the SEM sample image data after Fourier transform, the high-frequency and low-frequency components of the SEM sample image are determined. The thermionic signal voltage value acquisition module is used to acquire the thermionic signal voltage value corresponding to the scanning electron microscope sample image when the resolution is lower than a set threshold; the thermionic signal voltage value is the release voltage value generated when thermionic electrons are released; The high-frequency and low-frequency component adjustment factor determination module is used to determine the factors by which the high-frequency components are increased and the low-frequency components are decreased in the filtering algorithm based on the target relationship and the thermionic signal voltage value corresponding to the scanning electron microscope sample image. Specifically, it includes: determining the image signal-to-noise ratio of the scanning electron microscope sample image based on the target relationship and the thermionic signal voltage value corresponding to the scanning electron microscope sample image; and determining the factors by which the high-frequency components are increased and the low-frequency components are decreased in the Butterworth filtering algorithm based on the image signal-to-noise ratio. The target relationship is the relationship between the thermionic signal voltage value and the image signal-to-noise ratio. The image restoration module is used to restore the scanning electron microscope (SEM) sample image based on the multiple by which high-frequency components increase and low-frequency components decrease in the filtering algorithm. Specifically, it includes restoring the SEM sample image based on the multiple by which high-frequency components increase and low-frequency components decrease in the Butterworth filtering algorithm. The specific calculation formula is as follows: S(i,j)=H h (i,j)F(i,j)+H l (i,j)F(i,j); In the formula, H h (i,j) represents the high-pass filter function in the k-th order Butterworth filter function, H l (i,j) represents the low-pass filter function in the k-th order Butterworth filter function, r h and r l These represent the factors that increase the high-frequency components and decrease the low-frequency components, respectively. D0 represents the cutoff frequency, D(i,j) is the distance from point (i,j) to the origin of the filter, and the constant c is used to control the degree of sharpening of the scanning electron microscope image by the filter function.
6. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to enable the electronic device to perform the high-temperature scanning electron microscope image restoration method according to any one of claims 1 to 4.
7. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the high-temperature scanning electron microscope image restoration method as described in any one of claims 1 to 4.