Conductive carbon black lattice parameter extraction method and device and computer equipment

Abstract

This application relates to a method, apparatus, and computer device for extracting lattice parameters of conductive carbon black. The method includes: acquiring an original electron microscope image of conductive carbon black; obtaining a binarized image from the original electron microscope image; traversing the pixels in the binarized image that are assigned a value of 0, and obtaining valid pixels that meet preset conditions; performing a compensation operation on the binarized image: traversing the valid pixels, for each valid pixel, if the proportion of pixels assigned a value of 1 within a preset region corresponding to the valid pixel is less than a first threshold, then assigning the pixels assigned a value of 1 within the preset region to 0; if adjacent main regions meet the preset conditions, then bridging the adjacent main regions; extracting lattice fringes from the binarized image after the compensation operation and obtaining the shape parameters of the lattice fringes. This method can improve the accuracy of lattice fringe parameter extraction.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular to a method, apparatus and computer device for extracting conductive carbon black lattice parameters. Background Technology

[0002] Conductive carbon black is a typical type of specialty carbon black with specialized conductivity. Due to its excellent conductivity and high cost-effectiveness, it is widely used in many fields such as electronic devices, mining pipes, cable shielding materials, aerospace, petrochemicals, and power storage. Furthermore, compared to highly conductive fillers such as graphene and carbon nanotubes, conductive carbon black is increasingly favored in the energy sector due to its superior cost-effectiveness, for example, as a conductive agent in (power) lithium-ion batteries, lead-acid batteries, and supercapacitors.

[0003] Existing evaluation methods for conductive carbon black include high-resolution transmission electron microscopy (HRTEM), Raman spectroscopy, XRD (X-ray diffraction), infrared spectroscopy, and characterization of basic properties such as the oil absorption value of DBP (dibutyl phthalate). Currently, high-resolution transmission electron microscopy has been widely used in the analysis of the microscopic physicochemical structural features of carbon-based materials, such as aromatic hydrocarbon plates.

[0004] However, while high-resolution transmission electron microscopy (TEM) is an important tool for characterizing the microstructure of carbon-based materials, the information obtained from its images, beyond morphological observation, is quite limited, typically only allowing for qualitative analysis due to complex theoretical calculations. In quantitative analysis, current techniques for basic characterization of conductive carbon black yield limited results in calculating relevant parameters. For example, XRD calculations of the number of stacked layers and interlayer spacing of aromatic hydrocarbon sheets in conductive carbon black are inaccurate, and significant differences exist between different types of conductive carbon black (such as furnace black and acetylene black), while the differences between materials of the same type are relatively small and difficult to use as a reference. Therefore, the accuracy of quantitative analysis needs improvement. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, and computer equipment for extracting conductive carbon black lattice parameters that can improve the accuracy of stripe extraction, in order to address the above-mentioned technical problems.

[0006] Firstly, this application provides a method for extracting lattice parameters of conductive carbon black. The method includes:

[0007] Obtain the original electron microscope image of conductive carbon black, and then obtain a binarized image based on the original electron microscope image; each pixel in the binarized image is assigned a value of 1 or 0 respectively;

[0008] Traverse the pixels in the binarized image that are assigned a value of 0, and obtain the valid pixels that satisfy the preset rules;

[0009] Compensation operation is performed on the binarized image: traverse the valid pixels, and for each valid pixel, if the proportion of pixels with a value of 1 in the preset area corresponding to the valid pixel is less than a first threshold, then the pixels with a value of 1 in the preset area are assigned a value of 0; if adjacent main areas meet the preset conditions, then the adjacent main areas are bridged; wherein, the preset area is a fixed-shape area including a preset number of pixels, and the preset area contains the valid pixels corresponding to the preset area; the main area is composed of continuous valid pixels;

[0010] The lattice fringes are extracted from the binarized image after compensation, and the shape parameters of the lattice fringes are obtained.

[0011] In one embodiment, obtaining a binarized image from the original electron microscope image includes:

[0012] The original electron microscope image is cropped, and the cropped original electron microscope image is then subjected to image black and white contrast enhancement and noise reduction processing to obtain the first image;

[0013] The second image is obtained by performing a Fourier-inverse Fourier transform on the first image;

[0014] The second image is converted to grayscale to obtain the third image;

[0015] The third image is subjected to enhanced black-and-white contrast processing to obtain the fourth image;

[0016] The fourth image is binarized to obtain a binarized image.

[0017] In one embodiment, traversing the pixels assigned a value of 0 in the binarized image to obtain valid pixels includes:

[0018] Traverse the pixels in the binarized image that are assigned a value of 0. For each pixel assigned a value of 0, if the number of pixels assigned a value of 1 in the corresponding preset area is not greater than the second threshold, then assign a value of 0 to the pixels assigned a value of 1 in the preset area, and mark each pixel in the preset area as a valid pixel.

[0019] In one embodiment, the feature is that: the preset condition is that the number of interval pixels between two adjacent main regions is not greater than a third threshold; or there are island pixels between two adjacent main regions, and the number of interval pixels between two adjacent main regions is not greater than a fourth threshold; the interval pixels are pixels between two adjacent effective pixels located in adjacent main regions, according to the traversal order of effective pixels; the island pixels are pixels with a value of 0 between two adjacent effective pixels located in adjacent main regions, according to the traversal order of effective pixels.

[0020] In one embodiment, after assigning a value of 1 to pixels within a preset area and setting them to 0, the method further includes:

[0021] Mark the pixels in the preset area that were originally assigned a value of 1 and were later assigned a value of 0 as valid pixels, and update the main area;

[0022] Bridging adjacent main areas includes:

[0023] Pixels that were assigned a value of 1 between two adjacent main areas are assigned a value of 0 and marked as valid pixels.

[0024] In one embodiment, the method further includes:

[0025] The compensation operation is performed iteratively, updating the lattice fringes after each iteration. The iteration stops when the lattice fringes remain unchanged. The parameters of the lattice fringes are obtained based on the lattice fringes corresponding to the time when the iteration stops.

[0026] Secondly, this application provides a device for extracting conductive carbon black lattice parameters, the device comprising:

[0027] The image processing module is used to acquire the original electron microscope image of conductive carbon black and to obtain a binarized image based on the original electron microscope image; each pixel in the binarized image is assigned a value of 1 or 0 respectively;

[0028] The initial screening module is used to traverse the pixels with a value of 0 in the binarized image and obtain the valid pixels that meet the preset rules.

[0029] The compensation module is used to perform compensation operations on the binarized image: it iterates through the valid pixels, and for each valid pixel, if the proportion of pixels with a value of 1 in the preset area corresponding to the valid pixel is less than a first threshold, then the pixels with a value of 1 in the preset area are assigned a value of 0; if adjacent main areas meet the preset conditions, then the adjacent main areas are bridged; wherein, the preset area is a fixed-shape area including a preset number of pixels, and the preset area contains the valid pixels corresponding to the preset area; the main area is composed of continuous valid pixels;

[0030] The parameter acquisition module is used to extract lattice fringes from the binarized image after compensation and obtain the shape parameters of the lattice fringes.

[0031] Thirdly, this application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0032] Obtain the original electron microscope image of conductive carbon black, and then obtain a binarized image based on the original electron microscope image; each pixel in the binarized image is assigned a value of 1 or 0 respectively;

[0033] Traverse the pixels in the binarized image that are assigned a value of 0, and obtain the valid pixels that satisfy the preset rules;

[0034] Compensation operation is performed on the binarized image: traverse the valid pixels, and for each valid pixel, if the proportion of pixels with a value of 1 in the preset area corresponding to the valid pixel is less than a first threshold, then the pixels with a value of 1 in the preset area are assigned a value of 0; if adjacent main areas meet the preset conditions, then the adjacent main areas are bridged; wherein, the preset area is a fixed-shape area including a preset number of pixels, and the preset area contains the valid pixels corresponding to the preset area; the main area is composed of continuous valid pixels;

[0035] The lattice fringes are extracted from the binarized image after compensation, and the shape parameters of the lattice fringes are obtained.

[0036] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0037] Obtain the original electron microscope image of conductive carbon black, and then obtain a binarized image based on the original electron microscope image; each pixel in the binarized image is assigned a value of 1 or 0 respectively;

[0038] Traverse the pixels in the binarized image that are assigned a value of 0, and obtain the valid pixels that satisfy the preset rules;

[0039] Compensation operation is performed on the binarized image: traverse the valid pixels, and for each valid pixel, if the proportion of pixels with a value of 1 in the preset area corresponding to the valid pixel is less than a first threshold, then the pixels with a value of 1 in the preset area are assigned a value of 0; if adjacent main areas meet the preset conditions, then the adjacent main areas are bridged; wherein, the preset area is a fixed-shape area including a preset number of pixels, and the preset area contains the valid pixels corresponding to the preset area; the main area is composed of continuous valid pixels;

[0040] The lattice fringes are extracted from the binarized image after compensation, and the shape parameters of the lattice fringes are obtained.

[0041] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0042] Obtain the original electron microscope image of conductive carbon black, and then obtain a binarized image based on the original electron microscope image; each pixel in the binarized image is assigned a value of 1 or 0 respectively;

[0043] Traverse the pixels in the binarized image that are assigned a value of 0, and obtain the valid pixels that satisfy the preset rules;

[0044] Compensation operation is performed on the binarized image: traverse the valid pixels, and for each valid pixel, if the proportion of pixels with a value of 1 in the preset area corresponding to the valid pixel is less than a first threshold, then the pixels with a value of 1 in the preset area are assigned a value of 0; if adjacent main areas meet the preset conditions, then the adjacent main areas are bridged; wherein, the preset area is a fixed-shape area including a preset number of pixels, and the preset area contains the valid pixels corresponding to the preset area; the main area is composed of continuous valid pixels;

[0045] The lattice fringes are extracted from the binarized image after compensation, and the shape parameters of the lattice fringes are obtained.

[0046] The aforementioned method, apparatus, and computer equipment for extracting lattice parameters of conductive carbon black acquire a binarized image corresponding to the original electron microscope image of the conductive carbon black. Then, based on the binarized image, effective pixels are identified, and compensation operations are performed on the binarized image for these effective pixels. Pixels that might otherwise be assigned a value of 0 but are instead assigned a value of 1 are compensated to obtain a binarized image that more closely resembles the crystal fringe features in the original electron microscope image. Finally, the fringe parameter features are obtained based on this binarized image. This application improves the accuracy of lattice fringe parameter extraction by compensating for the crystal fringe feature information lost during the conversion of the original electron microscope image to a binarized image. Attached Figure Description

[0047] Figure 1 This is a diagram illustrating the application environment of a conductive carbon black lattice parameter extraction method in one embodiment.

[0048] Figure 2 This is a flowchart illustrating a method for extracting lattice parameters of conductive carbon black in one embodiment;

[0049] Figure 3 This is a parameter definition diagram of lattice fringes in one embodiment;

[0050] Figure 4 This is a comparative diagram showing the process before and after one iteration in one embodiment;

[0051] Figure 5This is a schematic diagram of the phased results of electron microscope raw image processing in one embodiment;

[0052] Figure 6 This is a bar chart showing the relationship between the length parameters and frequency of lattice fringes after skeleton extraction in one embodiment.

[0053] Figure 7 This is a bar chart showing the relationship between the curvature parameters and frequency of the lattice fringes after skeleton extraction in one embodiment.

[0054] Figure 8 This is a structural block diagram of a conductive carbon black lattice parameter extraction device in one embodiment;

[0055] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0057] The conductive carbon black lattice parameter extraction method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or located in the cloud or on other network servers. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. Server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0058] In one embodiment, such as Figure 2 As shown, a method for extracting lattice parameters of conductive carbon black is provided, which can be applied to... Figure 1 Taking terminal 102 as an example, the explanation includes the following steps:

[0059] Step 202: Obtain the original electron microscope image of the conductive carbon black, and obtain a binarized image based on the original electron microscope image; each pixel in the binarized image is assigned a value of 1 or 0 respectively.

[0060] The original electron microscope images were obtained by preparing a dispersion of conductive carbon black and ethanol in a certain ratio, followed by HRTEM testing.

[0061] Obtaining a binarized image from the original electron microscope image mainly utilizes binarization processing techniques. Binarization represents pixels in an image using two grayscale values, 0 and 255, thus giving the entire image a distinct black and white effect. A commonly used binarization method is thresholding. When a pixel's grayscale value is greater than a critical grayscale value, its grayscale value is set to the maximum value (255), representing it as white; conversely, if it is not greater than the critical grayscale value, its grayscale value is set to the minimum value (0), representing it as black, thereby achieving binarization. Each pixel in a binarized image has only two possibilities: black or white. For ease of calculation, black pixels are assigned a value of 0, and white pixels are assigned a value of 1. Thus, the original electron microscope image is transformed into a binarized image composed of 0s and 1s.

[0062] In this embodiment, the area formed by black pixels is the lattice fringes. By converting the raw electron microscope image into a binary image, the image representation can be simplified based on preprocessing, while highlighting the contour features of the lattice fringes, reducing interference from other irrelevant information, and making the image easier to analyze.

[0063] Step 204: Traverse the pixels in the binarized image that are assigned a value of 0, and obtain the valid pixels that satisfy the preset rules.

[0064] The process involves iterating through all pixels with a value of 0 in a binarized image in a specific order, and then filtering out the valid pixels. In this embodiment, the selection is primarily based on whether the area surrounding a pixel with a value of 0 has more pixels with a value of 0 than pixels with a value of 1. If the surrounding area is predominantly black, then this area can be considered a valid part of the image, and the resulting pixel is considered a valid pixel.

[0065] Step 206: Perform compensation operation on the binarized image: Traverse the valid pixels. For each valid pixel, if the proportion of pixels with a value of 1 in the preset area corresponding to the valid pixel is less than the first threshold, then assign a value of 0 to the pixels with a value of 1 in the preset area; if adjacent main areas meet the preset conditions, then bridge the adjacent main areas; wherein, the preset area is a fixed-shape area including a preset number of pixels, and the preset area contains the valid pixels corresponding to the preset area; the main area is composed of continuous valid pixels.

[0066] During the conversion of the raw electron microscope image into a binary image, some fringe feature information is lost, leading to distortion in subsequent fringe extraction. Therefore, this embodiment compensates for the lost fringe feature information through a compensation operation, thereby reducing the distortion rate and improving the accuracy of subsequent fringe extraction.

[0067] Step 206 involves traversing all valid pixels in the same order as in step 204. A common traversal order is from left to right and from top to bottom, with the traversal continuing from the next row after each row is completed.

[0068] The main area is a region composed of continuous effective pixels, that is, the main area is a whole black area in space.

[0069] When traversing each valid pixel, if the proportion of pixels with a value of 1 within the preset area corresponding to a valid pixel is less than a first threshold, then all pixels within that preset area need to be filled with 0. That is, pixels originally with a value of 1 within the preset area are assigned a value of 0, while pixels originally with a value of 0 are retained. Simultaneously, all pixels within that preset area are marked as valid pixels. If a newly marked valid pixel is adjacent to a main area, the main area is updated and expanded to include that valid pixel.

[0070] The following example illustrates the concept of each valid pixel corresponding to a 3×3 preset region centered on that valid pixel, with a first threshold set to 4. If the number of pixels with a value of 1 within the preset region is less than 4, then all pixels within the preset region are assigned a value of 0. If the number of pixels with a value of 1 within the preset prefetch is no greater than 4, then no changes are made.

[0071] The similarity of adjacent subject regions is determined by checking whether they meet preset conditions. This similarity determination is based on the following underlying logic: during the conversion of the original electron microscope image into a binary image, if feature loss causes a complete black area to be divided into two parts, becoming two independent subject regions, then these two subject regions generally have similar environments around the break point and are relatively close. Based on this logic, the similarity is determined. If the similarity is high, i.e., the preset conditions are met, then the two adjacent subject regions are bridged to merge them into a single subject region.

[0072] In this embodiment, the purpose of filling is to prune the stripes, which facilitates the subsequent clustering of black pixel clusters and ensures the accuracy of subsequent stripe extraction. The purpose of bridging is to connect two adjacent main regions that belong to the same main region into one, which also ensures the accuracy of subsequent stripe extraction.

[0073] Step 208: Extract lattice fringes from the binarized image after compensation and obtain the shape parameters of the lattice fringes.

[0074] The shape parameters of crystal fringes include the curvature, length, and area of ​​the crystal fringes. For example... Figure 3The diagram shows the definition of shape parameters for crystal fringes. Since the fringes are composed of curves, the curvature and length of the curves can be calculated. The curvature formula is curvature = R / L, where R represents the curve length and L represents the distance between the two endpoints of the curve. The fringe area is the area of ​​the surface enclosed by the curves.

[0075] Lattice fringe extraction from a binarized image primarily involves removing noisy pixels. The parameters of the lattice fringes are obtained by using skeleton extraction techniques to obtain the fringe skeleton, and then using the scale of the binarized image to determine the lattice fringe parameters.

[0076] Skeleton extraction, also known as image thinning, is a method that can thin a connected region to a width of one pixel, and is mainly used for extracting stripe features.

[0077] The scale of the binarized image and the pixels is obtained by counting the number of pixels in the binarized image. The length of a single pixel is then marked proportionally, and the parameters of the lattice fringes are obtained by counting the number of pixels in the skeleton.

[0078] This embodiment obtains a binarized image corresponding to the original electron microscope image of conductive carbon black. Then, based on the binarized image, effective pixels are identified, and compensation operations are performed on the binarized image for these effective pixels. Pixels that might have been assigned a value of 0 but were instead assigned a value of 1 are compensated to obtain a binarized image that more closely resembles the crystal fringe features in the original electron microscope image. Finally, fringe parameter features are obtained based on this binarized image. This application improves the accuracy of lattice fringe parameter extraction by compensating for the crystal fringe feature information lost during the conversion of the original electron microscope image to a binarized image.

[0079] In one embodiment, step 202, obtaining a binarized image from the original electron microscope image, includes: cropping the original electron microscope image; performing image contrast enhancement and noise reduction processing on the cropped original electron microscope image to obtain a first image; performing Fourier-inverse Fourier transform on the first image to obtain a second image; performing grayscale processing on the second image to obtain a third image; performing image contrast enhancement processing on the third image to obtain a fourth image; and performing binarization processing on the fourth image to obtain a binarized image.

[0080] The acquisition of the first, second, third, and fourth images all fall under the preprocessing stage. Preprocessing involves using image processing or drawing software to process the original electron microscope images, removing the background, and preserving and enhancing the lattice fringes of the conductive carbon black to facilitate the subsequent extraction of lattice parameters.

[0081] The first step is to import the raw electron microscope image into Adobe Photoshop. Select an area with minimal overlap of primary carbon black particles, minimal interlayer bonding between aromatic hydrocarbon plates, and as much integrity as possible. Crop this area into a square, the specific size depending on the particle size in the image, for example, 512×512 pixels. Enhance the black-and-white contrast of the image by adjusting contrast, saturation, and threshold, thus enhancing the stripe features. Convert the image to an image format supported by Digital Micrograph software, and import the converted image into Digital Micrograph software for noise reduction processing, further enhancing the stripe features to obtain the first image.

[0082] The second step involves using Digital Micrograph software to convert the data format of the first image to binary format. At this point, the thumbnails of the red, green, and blue channels are displayed in grayscale, and the image remains somewhat blurry. The pixel color parameters range from 0 to 255. A Fourier-inverse Fourier transform is then performed to extract the blurry crystal fringes, yielding the second image.

[0083] The purpose of this step is to convert the multi-channel color image into a single-channel grayscale image after the data format is changed to binary format, so that different grayscale levels can be constructed using the ratio of "0" and "1" to adjust the proportion of red, green and blue colors in the image.

[0084] The third step involves using floating-point operations to perform grayscale processing on the second image, enhancing the features of the crystal stripes three times, preserving the original structural information as much as possible and highlighting the stripe features to obtain the third image.

[0085] The formula for grayscale conversion is I(x,y)=0.3*I R (x,y)+0.59*I G (x,y)+0.11*I B (x,y), where I R (x,y) represents the red component of a pixel, I G (x,y) represents the green component in a pixel, I B (x,y) represents the blue component of a pixel, and I(x,y) is the grayscale value. This step can be implemented using OpenCV programming.

[0086] The fourth step involves importing the grayscale-processed third image back into Adobe Photoshop to adjust the threshold, enhance the black-and-white contrast, enhance the stripe features four times, and filter out noise to obtain the fourth image.

[0087] The fifth step is to perform binarization processing on the fourth image using OpenCV, converting the fourth image into a binary image.

[0088] Traditional semi-automated extraction methods based on computer languages ​​suffer from uncontrollable post-processing results and insufficient flexibility in image processing. For images with varying characteristics such as brightness, significant information loss and distortion that cannot be manually corrected during filtering and threshold adjustment for noise reduction can occur, leading to a high grayscale signal-to-noise ratio. This embodiment employs a preprocessing approach involving multiple manual enhancements of fringe features to preserve and highlight these features as much as possible, ensuring the accuracy of subsequent fringe extraction. Preprocessing serves as a foundation for subsequent fringe extraction, offering greater flexibility and acting as a step or prerequisite to reduce distortion.

[0089] In one embodiment, the method further includes segmenting the binarized image into blocks to obtain the positions of the main stripes, focusing the stripe extraction on areas where the stripes are more clearly defined. The block size can be selected according to the actual situation; for example, a binarized image of size 512×512 pixels can be segmented into blocks of size 64×64 pixels.

[0090] In one embodiment, step 204 includes: traversing the pixels in the binarized image that are assigned a value of 0; for each pixel assigned a value of 0, if the number of pixels assigned a value of 1 in the corresponding preset area is not greater than a second threshold, then assigning a value of 0 to the pixels assigned a value of 1 in the preset area, and marking each pixel in the preset area as a valid pixel.

[0091] For pixels assigned a value of 0, if the number of pixels assigned a value of 1 within the corresponding preset area is not greater than a second threshold, then the corresponding preset area is considered a valid part of the main subject; otherwise, the corresponding preset area is considered a non-valid part of the main subject. Pixels assigned a value of 1 within the valid part of the main subject are assigned a value of 0 to fill the corresponding preset area, and all pixels in this valid part of the main subject are marked as valid pixels. The second threshold serves as a pre-selection condition for the first threshold and is usually greater than the first threshold.

[0092] Let's take a 3×3 preset region centered on each pixel with a value of 0 as an example, and set the second threshold to 5. All pixels with a value of 0 in the image are identified. If the number of pixels with a value of 1 in the corresponding preset region is greater than 5, then the preset region is identified as a non-subject valid part and judged as noise. If the number of pixels with a value of 1 in the preset region is not greater than 5, then the preset region is identified as a subject valid part. Pixels with a value of 1 in the subject valid part are then filled by setting the value of 1 to 0 and marking them as valid pixels.

[0093] This embodiment improves the accuracy of subsequent crystal stripe feature extraction by identifying and determining the effective portion of the main body, thus compensating for feature information that may be lost during preprocessing. Simultaneously, the sieving process in this embodiment can filter out noise to a certain extent, allowing for further processing of effective pixels and reducing errors caused by subsequent compensation.

[0094] In one embodiment, the preset condition in step 206 is that the number of interval pixels between two adjacent main regions is not greater than a third threshold; or there are island pixels between two adjacent main regions, and the number of interval pixels between two adjacent main regions is not greater than a fourth threshold; the interval pixels are pixels between two adjacent effective pixels located in adjacent main regions, according to the traversal order of effective pixels; the island pixels are pixels with a value of 0 between two adjacent effective pixels located in adjacent main regions, according to the traversal order of effective pixels.

[0095] In this embodiment, the third threshold is greater than the fourth threshold. An isolated pixel is a pixel located between two adjacent main regions, assigned a value of 0, and not belonging to any main region; typically, there are one or two such pixels. A gap pixel is a pixel located between two adjacent valid pixels belonging to two different main regions, regardless of their assigned value. For example, if a valid pixel belonging to main region A is found, and another valid pixel belonging to main region B is found in the same row along a left-to-right traversal, and main regions A and B are adjacent, the number of pixels between these two valid pixels is the number of gap pixels. If there is a pixel with a value of 0 between these two adjacent valid pixels, and this pixel does not belong to any main region, then this pixel is an isolated pixel.

[0096] Let's take an example where each pixel corresponds to a preset 3×3 area centered on that pixel, and the third threshold is set to 10 and the fourth threshold to 4. Figure 4 Within the circled area, if the number of pixels between two adjacent main areas is no greater than 4, the two main areas are considered similar and can be bridged. If there are isolated pixels between two adjacent main areas, and the number of pixels between two adjacent main areas is no greater than 10, the two main areas are also similar and can be bridged.

[0097] This embodiment evaluates the distance between two adjacent main regions by measuring the number of interval pixels and isolated pixels, thereby setting the similarity determination conditions for the two main regions, and then performing the next bridging operation based on the preset conditions.

[0098] In one embodiment, after assigning a value of 1 to a pixel in the preset area to a value of 0 in step 206, the method further includes: marking the pixels in the preset area that were originally assigned a value of 1 but are now assigned a value of 0 as valid pixels and updating the main area; bridging adjacent main areas includes: assigning a value of 1 to a pixel between two adjacent main areas to a value of 0 and marking it as a valid pixel.

[0099] After assigning a value of 0 to pixels within a preset area that were previously set to 1, the transformed pixels are marked as valid pixels. This means that all pixels within the preset area corresponding to a valid pixel are marked as valid pixels, and the main body area is updated. A bridging determination is then made based on the updated main body area.

[0100] Bridging merges two main regions by assigning a value of 0 to the interval pixels between them, creating a single, larger main region. Simultaneously, the interval pixels between the two bridged main regions are marked as valid pixels.

[0101] This embodiment reduces the distortion caused during the conversion of the original electron microscope image into a binary image by bridging, thereby improving the accuracy of subsequent crystal stripe extraction.

[0102] In one embodiment, the method further includes: iteratively performing a compensation operation, updating the lattice fringes after each iteration, stopping the iteration when the lattice fringes remain unchanged, and obtaining the shape parameters of the lattice fringes based on the lattice fringes corresponding to the iteration stop.

[0103] Based on the previous iteration, all valid pixels are traversed again, and filling and bridging operations are performed. When the lattice fringes remain unchanged, the iteration stops, and the lattice fringes at this point are the lattice fringes to be extracted in step 208.

[0104] By continuously compensating for the lattice fringes through iterative operations, the range of the lattice fringes will become larger and more coherent, thus obtaining a reliable lattice fringe region, which is beneficial for subsequent extraction of parameters with higher accuracy.

[0105] In one embodiment, after the iteration is complete, it is possible that the binarized image still contains color patches generated near the fringes during the conversion of the original electron microscope image to a binarized image. These patches resemble miniature "islands" and are not considered valid pixels, not belonging to any main area. Therefore, these color patches are stripped and identified as noise for segmentation. This noise is ignored during subsequent lattice fringe extraction.

[0106] In one embodiment, the lattice fringes corresponding to the iteration stop are re-input into Adobe Photoshop to adjust the threshold and enhance the fringe features, and then imported into Digital Micrograph for noise reduction to reduce noise interference. Skeleton extraction is then performed based on the noise-reduced lattice fringes.

[0107] In one embodiment, OpenCV is used to extract the skeleton of the lattice fringes in step 208.

[0108] Traditional manual extraction methods involve converting and binarizing the raw electron microscope image to grayscale, followed by manual skeletalization and extraction of skeletal parameters using analysis and mapping software such as ArcGIS or ImageJ. This process is time-consuming, labor-intensive, and prone to significant errors. This embodiment, based on the binarized image after compensation, already yields relatively accurate skeletal features. Therefore, OpenCV programming is used to automatically extract the skeleton, effectively ensuring parameter extraction accuracy. Compared to manual extraction methods, this approach is more convenient, faster, and more accurate.

[0109] In one embodiment, step 208 performs skeletonization processing on the crystal fringes, proportionally marking the length of individual pixels according to the image scale. Since the length of the lattice fringes has the characteristic of (0.3nm to 2.86nm), based on this, the binarized image obtained by the iterative processing in step 206 is used to determine whether the pixel clusters in the blurred part belong to fringes or noise. Parts smaller than 0.3nm are deleted as noise, and pixel clusters larger than 0.28nm are determined as invalid fringes. All pixels are traversed in the same order as in steps 204 and 206. For example, if the current pixel coordinates are (X, Y), the coordinates of the next pixel can be obtained by incrementing X by one or Y by one when it is at the boundary. The above processing method is repeated until there are no lattice fringes in the entire image domain that exceed the range of 0.3nm to 2.86nm. Based on this, the parameters of the lattice fringes are calculated.

[0110] This invention uses carbon black as the analytical object and performs quantitative analysis of the lattice fringes of conductive carbon black based on high-resolution transmission electron microscopy. The image processing method is more flexible, highly automated, and has a simple operation process. At the same time, it optimizes the noise filtering method, optimizes the distortion effect of grayscale and binarization and multiple enhancement of crystal fringe features caused by binarization, and ensures the accuracy of crystal fringe extraction. It effectively avoids a large loss of useful information and can meet the needs of fast, accurate and flexible processing of HRTEM images, while ensuring the accuracy of aromatic hydrocarbon plate crystal fringe extraction.

[0111] The method proposed in this invention takes conductive carbon black as its starting point and is applicable to various carbon-based materials with microcrystalline structures or ordered lamination of aromatic hydrocarbon layers. It can quantitatively analyze effective information on the distribution of aromatic hydrocarbon layers in carbon-based materials.

[0112] like Figure 5 As shown, the partial stripe feature map of conductive carbon black obtained by the image processing method of the present invention has a good background stripping effect. It is worth noting that the extracted lattice stripes have a high degree of contrast and overlap with the original electron microscope image. At the same time, the structure of the lattice stripe edges is clear, with few burrs and adhesions. This indicates that the technical method of the present invention has excellent feature stripe extraction and background stripping effects and effectively avoids the distortion caused by the image processing process.

[0113] like Figure 6 As shown, the frequencies of lattice fringe lengths extracted using the image processing method provided by this invention in the ranges of 0–0.25 nm and 3.00 nm and above are 0, indicating that this invention can effectively filter noise. The lattice fringe length frequencies are mainly concentrated in the range of 0.28–1.50 nm, while the lattice fringe length frequencies in the range of 1.5–2.86 nm are low. Among them, the frequencies in the range of 0.28–0.75 nm are high, and the frequencies in the range of 0.75–1.5 nm are relatively high. This indicates that the corresponding regions in the HRTEM image segments of the selected embodiment of this invention are mainly composed of naphthalene rings and carbon 2×2 aromatic rings, followed by carbon 3×3 aromatic rings and carbon 4×4 aromatic rings. The proportion of carbon 5×5 and above aromatic rings is relatively small. This indicates that during the production process of this conductive carbon black embodiment, the crystal nuclei and multiple crystal nuclei of conductive carbon black are mainly composed of naphthalene rings and carbon 2×2 aromatic rings, while some aromatic rings will grow to further generate carbon 3×3 aromatic rings and carbon 4×4 aromatic rings.

[0114] like Figure 7 As shown, the lattice stripe curvature extracted by the image processing method provided by the present invention is mainly concentrated in the range of 0.10 to 0.45, and the frequency proportion of each range is not significantly different. This indicates that the aromatic stripe curvature distribution in the corresponding region of the HRTEM image segment of the selected embodiment of the present invention is not concentrated, which means that the uniformity of the layering structure between the aromatic hydrocarbon plates in the region of the conductive carbon black corresponding to the HRTEM image segment of the selected embodiment of the present invention is not high.

[0115] It should be understood that the cutting size, the shape and size of the preset area, and the values ​​of the first threshold, the second threshold, the third threshold, and the fourth threshold used in all the above embodiments can be set according to the actual situation. The specific values ​​disclosed in this invention are only one feasible solution and are not a limitation on the solution itself.

[0116] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0117] Based on the same inventive concept, this application also provides a conductive carbon black lattice parameter extraction device for implementing the above-described conductive carbon black lattice parameter extraction method. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the conductive carbon black lattice parameter extraction device provided below can be found in the limitations of the conductive carbon black lattice parameter extraction method described above, and will not be repeated here.

[0118] In one embodiment, such as Figure 8 As shown, a conductive carbon black lattice parameter extraction device is provided, comprising: an image processing module 802, a primary screening module 804, a compensation module 806, and a parameter acquisition module 808, wherein:

[0119] The image processing module 802 is used to acquire the original electron microscope image of conductive carbon black and to obtain a binarized image based on the original electron microscope image; each pixel in the binarized image is assigned a value of 1 or 0 respectively.

[0120] The initial screening module 804 is used to traverse the pixels with a value of 0 in the binarized image and obtain the valid pixels that meet the preset rules.

[0121] The compensation module 806 is used to perform compensation operations on the binarized image: it iterates through the valid pixels, and for each valid pixel, if the proportion of pixels with a value of 1 in the preset area corresponding to the valid pixel is less than a first threshold, then the pixels with a value of 1 in the preset area are assigned a value of 0; if adjacent main areas meet the preset conditions, then the adjacent main areas are bridged; wherein, the preset area is a fixed-shape area including a preset number of pixels, and the preset area contains the valid pixels corresponding to the preset area; the main area is composed of continuous valid pixels.

[0122] The parameter acquisition module 808 is used to extract lattice fringes from the binarized image after compensation and obtain the lattice fringes parameters.

[0123] The image processing module 802 is also used to crop the original electron microscope image, perform image enhancement and noise reduction processing on the cropped original electron microscope image to obtain a first image; perform Fourier-inverse Fourier transform on the first image to obtain a second image; perform grayscale processing on the second image to obtain a third image; perform image enhancement and noise reduction processing on the third image to obtain a fourth image; and perform binarization processing on the fourth image to obtain a binarized image.

[0124] The initial screening module 804 is also used to traverse the pixels with a value of 0 in the binarized image. For each pixel with a value of 0, if the number of pixels with a value of 1 in the corresponding preset area is not greater than the second threshold, then the pixels with a value of 1 in the preset area are assigned a value of 0, and each pixel in the preset area is marked as a valid pixel.

[0125] The preset conditions in the compensation module 806 are: the number of interval pixels between two adjacent main regions is not greater than the third threshold; or there are island pixels between two adjacent main regions, and the number of interval pixels between two adjacent main regions is not greater than the fourth threshold; the interval pixels are the pixels between two adjacent effective pixels located in adjacent main regions, according to the traversal order of the effective pixels; the island pixels are the pixels with a value of 0 between two adjacent effective pixels located in adjacent main regions, according to the traversal order of the effective pixels.

[0126] The compensation module 806 is also used to, after assigning a value of 1 to a pixel in the preset area to a value of 0, mark the pixel in the preset area that was originally assigned a value of 1 and is now assigned a value of 0 as a valid pixel and update the main area; and to assign a value of 1 to a pixel between two adjacent main areas to a value of 0 and mark it as a valid pixel.

[0127] The compensation module 806 is also used to iteratively perform the compensation operation, updating the lattice fringes after each iteration, and stopping the iteration when the lattice fringes remain unchanged; the parameter acquisition module 808 acquires the shape parameters of the lattice fringes based on the lattice fringes corresponding to the iteration stop.

[0128] Each module in the aforementioned conductive carbon black lattice parameter extraction device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0129] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores image data of conductive carbon black. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for extracting lattice parameters of conductive carbon black.

[0130] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0131] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement all of the above-described method embodiments.

[0132] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements all of the above method embodiments.

[0133] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements all of the above method embodiments.

[0134] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data shall comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0135] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0136] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0137] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for extracting lattice parameters of conductive carbon black, characterized in that, The method includes: Obtain the original electron microscope image of conductive carbon black, and obtain a binarized image based on the original electron microscope image; each pixel in the binarized image is assigned a value of 1 or 0 respectively; Traverse the pixels in the binarized image that are assigned a value of 0. For each pixel assigned a value of 0, if the number of pixels assigned a value of 1 in the preset area corresponding to the pixel is not greater than a second threshold, then assign a value of 0 to the pixels assigned a value of 1 in the preset area, and mark each pixel in the preset area as a valid pixel. A compensation operation is performed on the binarized image: traversing the valid pixels, for each valid pixel, if the proportion of pixels with a value of 1 in the preset region corresponding to the valid pixel is less than a first threshold, then the pixels with a value of 1 in the preset region are assigned a value of 0; if adjacent main regions meet preset conditions, then the adjacent main regions are bridged; wherein, the preset region is a fixed-shape region including a preset number of pixels, and the preset region contains the valid pixels corresponding to the preset region; the main region is composed of continuous valid pixels; Based on the binarized image after compensation, extract the lattice fringes and obtain the shape parameters of the lattice fringes.

2. The method according to claim 1, characterized in that, The step of obtaining a binarized image from the original electron microscope image includes: The original electron microscope image is cropped, and the cropped original electron microscope image is subjected to image black and white contrast enhancement and noise reduction processing to obtain a first image; The second image is obtained by performing a Fourier-inverse Fourier transform on the first image; The second image is converted to grayscale to obtain the third image; The third image is subjected to enhanced black-and-white contrast processing to obtain the fourth image; The fourth image is binarized to obtain the binarized image.

3. The method according to claim 1, characterized in that: The preset conditions are: the number of interval pixels between two adjacent main regions is not greater than a third threshold; or there are island pixels between two adjacent main regions, and the number of interval pixels between two adjacent main regions is not greater than a fourth threshold; the interval pixels are pixels between two adjacent effective pixels located in adjacent main regions, according to the traversal order of the effective pixels; the island pixels are pixels with a value of 0 between two adjacent effective pixels located in adjacent main regions, according to the traversal order of the effective pixels.

4. The method according to claim 1, characterized in that, After assigning a value of 1 to the pixels within the preset area and setting them to 0, the method further includes: The pixels in the preset area that were originally assigned a value of 1 and were later assigned a value of 0 are marked as valid pixels, and the main area is updated. The bridging of adjacent main regions includes: Pixels that were previously assigned a value of 1 between two adjacent main regions are assigned a value of 0 and marked as valid pixels.

5. The method according to any one of claims 1 to 4, characterized in that, The method further includes: The compensation operation is performed iteratively, and the lattice fringes are updated after each iteration. The iteration stops when the lattice fringes remain unchanged. The shape parameters of the lattice fringes are obtained based on the lattice fringes corresponding to the time when the iteration stops.

6. A device for extracting lattice parameters of conductive carbon black, characterized in that, The device includes: The image processing module is used to acquire the original electron microscope image of conductive carbon black, and to acquire a binarized image based on the original electron microscope image; each pixel in the binarized image is assigned a value of 1 or 0 respectively; The initial screening module is used to traverse the pixels in the binarized image that are assigned a value of 0. For each pixel assigned a value of 0, if the number of pixels assigned a value of 1 in the preset area corresponding to the pixel is not greater than a second threshold, then the pixels assigned a value of 1 in the preset area are assigned a value of 0, and each pixel in the preset area is marked as a valid pixel. The compensation module is used to perform a compensation operation on the binarized image: traversing the effective pixels, for each effective pixel, if the proportion of pixels with a value of 1 in the preset area corresponding to the effective pixel is less than a first threshold, then the pixels with a value of 1 in the preset area are assigned a value of 0; if adjacent main areas meet preset conditions, then the adjacent main areas are bridged; wherein, the preset area is a fixed-shape area including a preset number of pixels, and the preset area contains the effective pixels corresponding to the preset area; the main area is composed of continuous effective pixels; The parameter acquisition module is used to extract lattice fringes from the binarized image after compensation and obtain the shape parameters of the lattice fringes.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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

Images