A Correction and Stitching Method for EBSD Data

Abstract

The invention provides a correction and splicing method for EBSD data, and relates to the technical field of EBSD data processing. The method first converts EBSD data into an orientation imaging map, splices and corrects the orientation imaging map to obtain a standard map and a map to be corrected, matches and aligns and screens out effective feature data points of the standard map and the map to be corrected, calculates a target transformation matrix for correcting distorted pixel points in the map to be corrected back to a standard position through effective feature data points, performs the same matrix transformation on identifiable data points in each orientation imaging map in the orientation imaging map data set to obtain corrected data points, and standardizes the format of the corrected data points to obtain corrected and standardized EBSD data, splices all EBSD data, determines whether the splicing is completely spliced, and if there is a splicing mark, re-matches the feature data points, and repeats this process until the spliced ​​data has no splicing mark.

Description

Technical Field

[0001] The invention relates to the technical field of EBSD data processing, and in particular to a correction and splicing method for EBSD data. Background Art

[0002] Electron Backscattered Diffraction (EBSD) is an important material characterization technique used in scanning electron microscopes (SEM). It obtains the orientation and structural information of crystal micro-areas by analyzing the diffraction patterns generated by high-energy electrons reflected from the sample surface. In recent years, with the revolutionary upgrade of the detector acquisition frequency, the analysis rate can exceed 4500pps, and the microstructure and texture of the material can be quickly and quantitatively studied without the need for high beam current or sacrificing pattern resolution. This means that EBSD technology can get rid of the barriers of micro-area detection and perform fine characterization of the texture, grain orientation, microstructure, grain size and morphology of large-area samples. At the same time, with the increasing stringency of scientific research statistics, EBSD jigsaw puzzle technology is becoming more and more widely used. However, in order to ensure high spatial resolution during EBSD testing, dynamic focusing technology is mostly used, which refers to the function of automatically repairing the difference in focusing of the electron beam at the center and four corners of the screen when the electron gun scans the screen. When focusing, ordinary electron guns will produce astigmatism, that is, the focal lengths of the pixels in the vertical and horizontal directions are not uniform at the corners. The astigmatism is most obvious around the screen. In order to reduce the occurrence of this situation, the electron gun needs to be dynamically compensated so that the average of any scanning point on the screen can be clear and consistent. It periodically generates a focusing voltage with a special waveform, so that the voltage is the lowest when the electron beam is at the center point, and the voltage gradually increases with the increase of the focal length when scanning the corners, so as to correct the focus changes at any time. In this dynamic correction process, it is easy to cause distortion of the scanned data.

[0003] At present, in order to ensure that the obtained data can be fully and truly represented, the image overlap rate must generally be greater than 15% to obtain reliable results during EBSD stitching, which greatly restricts the efficiency of detection. With the continuous expansion of the demand for the detection sample area in scientific research statistics, the sample magnification during detection must also be reduced accordingly. The small magnification acquisition of the Charge Coupled Device (CCD) camera further magnifies the difference in vertical focal length. It is difficult for existing EBSD technology to accurately stitch samples below 500 times; at the same time, the EBSD being detected will show inconsistent left and right lengths at all detection magnifications due to inconsistent left and right focusing voltages, resulting in repeated data points in the current stitching methods. Summary of the invention

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: a correction and splicing method of EBSD data, comprising the following steps:

[0005] Step 1: Collect EBSD data of the target material and generate an orientation imaging map, establish an orientation imaging map data set of the target material, stitch and correct any orientation imaging map to obtain a standard map, and the corresponding uncorrected image is the image to be corrected;

[0006] Step 1.1: Perform EBSD mosaic detection experiments on the target material area of ​​interest to obtain multiple EBSD data, use EBSD data processing software to obtain the orientation image of each EBSD data and the orientation and position information of each identifiable pixel in the orientation image, establish the mapping relationship between EBSD data points and orientation image, and form an orientation image data set;

[0007] Step 1.2: Perform stitching and correction processing on any orientation image and its adjacent orientation image on the left or right to obtain a standard image. The orientation image before correction is the image to be corrected;

[0008] Step 2: Match and align the feature data points of the standard image and the image to be corrected and select the valid feature data points. Match the geometric transformation between the standard image and the image to be corrected according to the position information of the valid feature data points and calculate the target transformation matrix.

[0009] Step 2.1: grayscale the standard image and the image to be corrected, identify the characteristic data points in the standard image and the image to be corrected, match the characteristic data points that meet the required quantity and quality by setting the quality threshold of the characteristic data points, the spatial scale of detection, and the detection accuracy, record the positions of the characteristic data points, and align the same characteristic data points in the standard image and the image to be corrected;

[0010] Step 2.2: Based on the aligned feature data points, screen the valid feature data points in the standard image and the image to be corrected, match the geometric transformation between the standard image and the image to be corrected based on the screened valid feature data points, and calculate the target transformation matrix for correcting the distorted pixels in the image to be corrected back to the standard position;

[0011] The target transformation matrix is ​​calculated based on the matchFeatures function, and the specific process is:

[0012] Step 2.2.1: Screen the valid feature data points in the standard image and the image to be corrected;

[0013] The standard image and the image to be corrected are set as shown in the following formula:

[0014] ,in, and They correspond to the standard image and the image to be corrected, represents the jth feature descriptor in the i-th image, i=1, 2, j=1, 2, 3...m;

[0015] Standard diagram Each feature descriptor in Find the closest and second closest feature descriptors , and their distances are and , using the ratio test to determine the feature descriptor Is it a valid feature data point for subsequent calculations? / <threshold, then the feature descriptor is considered In the standard diagram If it is valid, it is counted as a valid feature data point, otherwise the feature descriptor is not recorded ; Image to be corrected Methods and standard diagrams for selecting effective feature data points The screening method is the same;

[0016] Step 2.2.2: Calculate the target change matrix using the transformation relationship between the coordinates of the valid feature data points in the standard image and the coordinates of the valid feature data points in the image to be corrected that are aligned with the valid feature data points in the standard image;

[0017] Set the change matrix of the orientation imaging map to T, and use the least squares method to evaluate the change matrix. The goal is to find the most accurate transformation matrix T so that , and then get the target transformation matrix;

[0018] in, and is the coordinate of the valid feature data point in the standard graph, and The coordinates of the valid feature data points in the image to be corrected that are aligned with the valid feature data points in the standard image;

[0019] Step 3: Use the target transformation matrix to perform the same matrix transformation on the identifiable data points in each orientation imaging image in the orientation imaging image data set to obtain the corrected data points, and standardize the format of the corrected data points to obtain the corrected and standardized EBSD data. Puzzle all the corrected and standardized EBSD data to determine whether the splicing is completely spliced. If there is a splicing mark, return to step 2 to re-match the feature data points and recalculate the target transformation matrix until the spliced ​​data has no splicing marks;

[0020] Step 3.1: Using the target transformation matrix obtained in step 2.2, perform the same matrix transformation on the identifiable data points in each orientation imaging image in the orientation imaging image data set to obtain the corrected data points and standardize the format of the corrected data points to obtain the corrected and standardized EBSD data;

[0021] Step 3.2: Export the corrected and standardized EBSD data and stitch the corrected and standardized EBSD data points;

[0022] Step 3.3: Determine whether there are any splicing marks at the splicing point. If there are splicing marks, return to step 2.1, adjust the quality threshold, spatial scale and detection accuracy, and re-match the required number and quality of feature data points;

[0023] Step 3.4: Re-execute steps 2.2 to 3.3 until there are no traces of splicing in the spliced ​​EBSD data.

[0024] The beneficial effects of adopting the above technical solution are:

[0025] (1) When processing EBSD data, the present invention performs program correction on data points affected by dynamic focusing to achieve pixel-level data correction, which can realize EBSD splicing under conditions of small overlap rate, saving detection cost and test machine time and improving detection efficiency.

[0026] (2) The corrected and stitched EBSD data of the present invention is not affected by dynamic focus distortion, and has no requirement on the stitching multiple when stitching a large area. Large-area EBSD data stitching can be achieved under small field of view conditions, thus broadening the application scope of EBSD detection.

[0027] (3) The EBSD data after correction and splicing in the present invention has no repeated data points, thereby improving the authenticity and accuracy of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A flow chart of a correction and splicing method for EBSD data provided by an embodiment of the present invention;

[0029] Figure 2 An EBSD stitching diagram using the AZtec system's own stitching method provided in an embodiment of the present invention;

[0030] Figure 3 An EBSD stitching image using the correction stitching method of the present invention provided in an embodiment of the present invention;

[0031] Figure 4 The orientation imaging diagram measured by EBSD provided in the embodiment of the present invention;

[0032] Figure 5This is an orientation imaging diagram of the correction method of the present invention provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0034] In this embodiment, a method for stitching and correcting EBSD data is provided. After obtaining the tested EBSD data, the orientation imaging diagram thereof is extracted, a standard diagram is obtained by using image processing software, and then an overall transformation matrix is ​​obtained by using image registration technology, thereby realizing the correct stitching of EBSD data points. Figure 1 As shown, the following steps are included:

[0035] Step 1: Use the EBSD mosaic detection experiment to collect EBSD data of the target material and generate an orientation imaging map, determine the orientation and position information of the identifiable pixel points of the orientation imaging map, establish an orientation imaging map data set of the target material, and use image processing software to stitch and correct any orientation imaging map to obtain a standard map. The corresponding uncorrected image is the image to be corrected;

[0036] Step 1.1: Perform EBSD mosaic test on the target material area of ​​interest, setting the mosaic matrix to be larger than , obtain multiple EBSD data, apply EBSD data processing software (crystal and Channel 5) to obtain the orientation image of each EBSD data and the orientation and position information of each identifiable pixel in the orientation image, establish the mapping relationship between EBSD data points and orientation image, and form the orientation image data set;

[0037] Step 1.2: Use image processing software to perform stitching and correction processing on any orientation image and its adjacent orientation image on the left or right to obtain a standard image. The orientation image before correction is the image to be corrected;

[0038] Step 2: Use image registration technology to match and align the feature data points of the standard image and the image to be corrected and filter out the valid feature data points. According to the position information of the valid feature data points, match the geometric transformation between the standard image and the image to be corrected and calculate the target transformation matrix.

[0039] Step 2.1: Use image processing software to grayscale the standard image and the image to be corrected, and use image registration technology to identify the characteristic data points in the standard image and the image to be corrected. By setting the quality threshold (MetricThreshold) of the characteristic data points, the spatial scale (NumOctaves) of the detection, and the detection accuracy (NumOctaves), match the characteristic data points that meet the required quantity and quality, record the position of the characteristic data points, and align the same characteristic data points in the standard image and the image to be corrected;

[0040] Step 2.2: Based on the aligned feature data points, screen the valid feature data points in the standard image and the image to be corrected, match the geometric transformation between the standard image and the image to be corrected based on the screened valid feature data points, and calculate the target transformation matrix for correcting the distorted pixels in the image to be corrected back to the standard position;

[0041] The target transformation matrix is ​​calculated based on the matchFeatures function, and the specific process is:

[0042] Step 2.2.1: Screen the valid feature data points in the standard image and the image to be corrected;

[0043] The standard image and the image to be corrected are set as shown in the following formula:

[0044] ,in, and They correspond to the standard image and the image to be corrected, represents the jth feature descriptor in the i-th image;

[0045] Standard diagram Each feature descriptor in Find the closest and second closest feature descriptors , and their distances are and In order to improve the robustness of matching and eliminate fuzzy matching, the ratio test is used to determine the feature descriptor. Is it a valid feature data point for subsequent calculations? / <threshold, then the feature descriptor is considered In the standard diagram If it is valid, it is counted as a valid feature data point, otherwise the feature descriptor is not recorded ; Image to be corrected The method of selecting effective feature data points is the same;

[0046] Step 2.2.2: Calculate the target change matrix using the transformation relationship between the coordinates of the valid feature data points in the standard image and the coordinates of the valid feature data points in the image to be corrected that are aligned with the valid feature data points in the standard image;

[0047] Set the change matrix of the orientation imaging map to T, where T is a three-dimensional numerical matrix, and use the least squares method to evaluate the change matrix. The goal is to find the most accurate transformation matrix T so that , and then get the target transformation matrix;

[0048] in, and is the coordinate of the valid feature data point in the standard graph, and The coordinates of the valid feature data points in the image to be corrected that are aligned with the valid feature data points in the standard image;

[0049] Step 3: Use the target transformation matrix to perform the same matrix transformation on the identifiable data points in each orientation imaging image in the orientation imaging image data set to obtain the corrected data points, and standardize the format of the corrected data points to obtain the corrected and standardized EBSD data. Use the jigsaw puzzle program (MTEX) to puzzle all the corrected and standardized EBSD data, and then use the image processing software to determine whether the splicing is completely spliced. If there is a splicing mark, return to step 2 to re-match the feature data points, and then execute steps 2 to 3, and repeat this process until there is no splicing mark in the spliced ​​data;

[0050] Step 3.1: Using the target transformation matrix obtained in step 2.2, perform the same matrix transformation on the identifiable data points in each orientation imaging image in the orientation imaging image data set to obtain the corrected data points and standardize the format of the corrected data points to obtain the corrected and standardized EBSD data;

[0051] Step 3.2: Export the corrected and standardized EBSD data using the export function in the MTEX ​​program, and use the automatic mosaic function of the MTEX ​​program to stitch the corrected and standardized EBSD data points;

[0052] Step 3.3: Use image processing software to determine whether there are any splicing marks at the splicing point. If there are splicing marks, return to step 2.1, adjust the quality threshold (MetricThreshold), spatial scale (NumOctaves) and detection accuracy (NumOctaves), and re-match the required number and quality of feature data points;

[0053] Step 3.4: Re-execute steps 2.2 to 3.3 until there are no traces of splicing in the spliced ​​EBSD data;

[0054] In this example, the EBSD stitching diagram obtained by the stitching method provided by the AZtec system is compared with the EBSD data correction stitching method of the present invention. Figure 2 and Figure 3 , when the test multiples are all 100 times, Figure 2 The stitching method provided by the AZtec system has many repeated data points in the stitched image, leaving stitching seams in the image and step-like shapes at the bottom of the image. Figure 3 The repeated data points in the spliced ​​image obtained by the method of the present invention have been corrected, and there are no splicing marks and step morphology.

[0055] Compare the orientation image measured in step 1 with the orientation image corrected by the present invention, and see Figure 4 and Figure 5 ,from Figure 4 It can be seen that the left and right sides of the orientation imaging image are distorted inconsistently due to the influence of dynamic focusing. Figure 5 It can be seen that the distortion has been corrected.

[0056] The method of the present invention is used to process EBSD data, and the data points affected by dynamic focusing are corrected by program, and pixel-level data correction is achieved, which can realize the splicing of EBSD under the condition of small overlap rate, and greatly save the detection cost. At present, in order to eliminate the influence of dynamic focusing, the overlap rate of test data can only be increased. The overlap rate recommended by the AZtec system is 20%. The present invention can reduce the overlap rate to 10%, and the puzzle data finally obtained has no repeated data points at all, avoiding errors in data statistics. In addition, the method of the present invention avoids the influence of dynamic focusing on the test results, and therefore can be tested under the condition of 25 times the minimum field of view, and the efficiency is improved by nearly 400 times under the condition of 500 times the field of view recommended by the AZtec test system, which greatly broadens the application range of EBSD detection.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope defined by the claims of the present invention.

Claims

1. A correction and splicing method for EBSD data, characterized by: The following steps are involved: Step 1: Collect EBSD data of the target material and generate an orientation imaging map, establish an orientation imaging map data set of the target material, stitch and correct any orientation imaging map to obtain a standard map, and the corresponding uncorrected image is the image to be corrected; Step 2: Match and align the feature data points of the standard image and the image to be corrected, and filter out the valid feature data points. Match the geometric transformation between the standard image and the image to be corrected according to the position information of the valid feature data points, and calculate the target transformation matrix. Step 2.1: grayscale the standard image and the image to be corrected, identify the characteristic data points in the standard image and the image to be corrected, match the characteristic data points that meet the required quantity and quality by setting the quality threshold of the characteristic data points, the spatial scale of detection, and the detection accuracy, record the positions of the characteristic data points, and align the same characteristic data points in the standard image and the image to be corrected; Step 2.2: Based on the aligned feature data points, screen the valid feature data points in the standard image and the image to be corrected, match the geometric transformation between the standard image and the image to be corrected based on the screened valid feature data points, and calculate the target transformation matrix for correcting the distorted pixels in the image to be corrected back to the standard position; Step 3: Use the target transformation matrix to perform the same matrix transformation on each identifiable data point in the orientation imaging image data set to obtain the corrected data point, and standardize the format of the corrected data point to obtain the corrected and standardized EBSD data. Puzzle all the corrected and standardized EBSD data to determine whether the splicing is completely spliced. If there is a splicing mark, return to step 2 to re-match the feature data points and recalculate the target transformation matrix until the spliced ​​data has no splicing marks.

2. The correction and splicing method of EBSD data according to claim 1, characterized in that: The specific method of step 1 is: Step 1.1: Perform EBSD mosaic detection experiments on the target material area of ​​interest to obtain multiple EBSD data, use EBSD data processing software to obtain the orientation image of each EBSD data and the orientation and position information of each identifiable pixel in the orientation image, establish the mapping relationship between EBSD data points and orientation image, and form an orientation image data set; Step 1.2: Perform stitching and correction processing on any orientation imaging image and its adjacent orientation imaging image on the left or right to obtain a standard image. The orientation imaging image before correction is the image to be corrected.

3. The correction and splicing method of EBSD data according to claim 1, characterized in that: The target transformation matrix of step 2.2 is calculated based on the matchFeatures function, and the specific process is: Step 2.2.1: Screen the valid feature data points in the standard image and the image to be corrected; Step 2.2.2: Calculate the target change matrix using the transformation relationship between the coordinates of the valid feature data points in the standard image and the coordinates of the valid feature data points in the image to be corrected that are aligned with the valid feature data points in the standard image.

4. The correction and splicing method of EBSD data according to claim 3, characterized in that: The specific method of step 2.2.1 is: The standard image and the image to be corrected are set as shown in the following formula: F1={f 1,1 ,f 1,2 ,…f 1,m } F2={f 2,1 ,f 2,2 ,…f 2,m } Among them, F1 and F2 correspond to the standard image and the image to be corrected respectively, f i,j represents the jth feature descriptor in the i-th image, i = 1, 2, j = 1, 2, 3...m; For each feature descriptor f in the standard graph F1 1,j Find the closest and second closest feature descriptors f 1,t ,f 1,k , whose distances are d1 and d2 respectively, and the ratio test is used to determine the feature descriptor f 1,j Is it a valid feature data point for subsequent calculations? If d1 / d2<threshold, then the feature descriptor f 1,j If it is valid in the standard graph F1, it is counted as a valid feature data point. Otherwise, the feature descriptor f is not recorded. 1,j The method of screening effective feature data points in the image to be corrected F2 is the same as that of the standard image F1.

5. The method for correcting and stitching EBSD data according to claim 4, characterized in that: The specific method of step 2.2.2 is: Set the change matrix of the orientation imaging map to T, and use the least squares method to evaluate the change matrix. The goal is to find the most accurate transformation matrix T so that Then the target transformation matrix is ​​obtained; Among them, x 1,h With y 1,h is the coordinate of the valid feature data point in the standard graph, x 2,h With y 2,h The coordinates of the valid feature data points in the image to be corrected that are aligned with the valid feature data points in the standard image.

6. The method for correcting and stitching EBSD data according to claim 5, characterized in that: The specific method of step 3 is: Step 3.1: Using the target transformation matrix obtained in step 2.2, perform the same matrix transformation on the identifiable data points in each orientation imaging image in the orientation imaging image data set to obtain the corrected data points and standardize the format of the corrected data points to obtain the corrected and standardized EBSD data; Step 3.2: Export the corrected and standardized EBSD data and stitch the corrected and standardized EBSD data points; Step 3.3: Determine whether there are any splicing marks at the splicing point. If there are splicing marks, return to step 2.1, adjust the quality threshold, spatial scale and detection accuracy, and re-match the required number and quality of feature data points; Step 3.4: Re-execute steps 2.2 to 3.3 until there are no traces of splicing in the spliced ​​EBSD data.

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