CN114119343A - Dark field image storage method utilizing sparse matrix characteristic - Google Patents
Dark field image storage method utilizing sparse matrix characteristic Download PDFInfo
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Abstract
The invention discloses a dark field image storage method utilizing sparse matrix characteristics, which comprises the following steps: (1) scanning and imaging the sample to be detected by utilizing a dark field imaging system; the dark field imaging system comprises: the device comprises a three-axis displacement table for placing a sample to be measured, a light source module for providing oblique illumination light for the sample to be measured, an imaging lens group and a CCD camera, wherein the imaging lens group is positioned right above the sample to be measured and used for receiving scattered light; a dark field image formed by the CCD camera is transmitted to a computer for processing, and the computer is also connected with the three-axis displacement table and used for controlling the accurate movement of the three-axis displacement table; (2) selectively compressing the original image data acquired by the dark field imaging system by using a sparse matrix structure; (3) and extracting data in line units, and finishing the positions of head and tail elements of single-line data by searching and positioning so as to finish the extraction of the whole line of data and further finish the final output of the whole image. The invention can improve the space storage efficiency and the time index efficiency of the image data.
Description
Technical Field
The invention belongs to the technical field of optics, and particularly relates to a dark field image storage method utilizing sparse matrix characteristics.
Background
The dark field scattering imaging system mostly adopts the schemes of shifting an objective diaphragm, using an annular objective diaphragm or oblique illumination and the like, and ensures that only the scattered light of a target object can be captured and imaged by a sensor, so that the dark field scattering imaging system can obtain extremely high contrast. Therefore, the dark field imaging system is widely applied to an optical system with high imaging requirements.
Dark field scattering has a great advantage over imaging schemes such as AFM (atomic Force microscope), SEM (scanning Electron microscope), etc.
For example, chinese patent publication No. CN111292241A discloses a method for split-scanning and splicing large-aperture optical elements, which includes: (1) dividing a region to be detected into a plurality of sub-regions according to the area of the region to be detected of the optical element; (2) dividing each sub-region into a plurality of sub-apertures according to the area of the sub-region; (3) selecting a scanning starting point, and performing sub-aperture snake-shaped scanning on all sub-apertures in a starting point sub-area by using an acquisition camera to complete scanning and perform sub-aperture splicing on the sub-apertures in the sub-area; (4) sequentially scanning all the sub-regions by adopting a sub-region snake scanning method, and scanning and splicing in each sub-region by adopting the sub-aperture snake scanning and sub-aperture splicing method which is the same as that in the step (3); (5) and after the scanning work of the adjacent sub-regions is finished, splicing the sub-regions until all the regions are scanned, and finally obtaining a complete image of the region to be detected.
The method completes the detection of the optical element through the scheme of scanning and splicing the sub-aperture image, and further expands the application of dark field imaging in a large-caliber high-resolution detection scene. With the further improvement of the detection requirement, the number of the pictures generated by scanning is more and more, and the huge data volume not only increases the cost of the corresponding hardware requirement, but also reduces the response speed of the whole system.
Therefore, as the imaging requirement is continuously increased, the dark field picture storage efficiency is very important in order to ensure the smooth operation and good user experience of the whole system.
Disclosure of Invention
The invention provides a dark field image storage method utilizing sparse matrix characteristics, so that the space storage efficiency and the time index efficiency of image data are improved.
A dark field image storage method utilizing sparse matrix characteristics comprises the following steps:
(1) scanning and imaging the sample to be detected by utilizing a dark field imaging system;
the dark field imaging system comprises: the device comprises a three-axis displacement table for placing a sample to be measured, a light source module for providing oblique illumination light for the sample to be measured, an imaging lens group and a CCD camera, wherein the imaging lens group is positioned right above the sample to be measured and used for receiving scattered light; a dark field image formed by the CCD camera is transmitted to a computer for processing, and the computer is also connected with the three-axis displacement table and used for controlling the accurate movement of the three-axis displacement table;
(2) selectively compressing the original image data acquired by the dark field imaging system by using a sparse matrix structure;
(3) and extracting data in line units, and finishing the positions of head and tail elements of single-line data by searching and positioning so as to finish the extraction of the whole line of data and further finish the final output of the whole image.
Further, in the step (1), a specific process of performing scanning imaging on the sample to be measured by using the dark field imaging system is as follows:
a sample to be detected is placed on a three-axis displacement table, and the three-axis displacement table enables the sample to accurately move in three directions, so that the system can be ensured to scan in a large range;
the light source module obliquely enters the system at a certain angle to illuminate the sample to be measured, and dark field scattering imaging is realized in an oblique illumination mode: when the sample is free of defects, the illumination ray A will be reflected at the sample surface and leave the system along the path A'; when incident light irradiates a defect, strong scattered light A' is generated on the surface of a sample, a part of light is captured by a photosensitive area of a CCD camera through an imaging lens group, and cluster white points are formed on an image;
finally, the image shot by the CCD camera is a dark field scattering image with a large-range black background and clustered white spots of different sizes distributed unevenly.
In the step (2), as the overall background of the original dark field picture is black, defects and damages which are represented as irregular white clustered bright spots are distributed, in order to deal with the characteristic of irregular distribution of valuable information, a compression sparse line CSR mode is used for data compression, and the data structure is flexible enough on the premise of ensuring a certain compression rate. The specific process is as follows:
(2-1) traversing the test picture set, and determining a gray threshold t;
(2-2) initializing a result of the compressed sparse row CSR, wherein the result comprises an array A and a binary array B;
(2-3) traversing original image data acquired by the dark field imaging system, and if the corresponding pixel value is greater than t, marking the pixel as valuable;
(2-4) extracting (x, value) values of the valuable pixels and storing the values into a binary array B, wherein x represents the number of columns of the corresponding pixel points, and value represents the gray value of the corresponding pixel points;
and (2-5) extracting the y value of the first valuable pixel in each row, and storing the y value into an array A, wherein y represents the row number of the corresponding pixel point.
In addition, the sparse matrix needs to be ensured to obtain positive benefits when the density is lower than a certain level, and under extreme conditions, partial dark field pictures may cause overexposure of the whole image due to overlarge defects and the like, so that the density requirement is not met. While sparse matrices lead to a sudden drop in performance as the density increases. In order to deal with the situation, when the sparse matrix structure is used for selectively compressing data, whether data compression is performed or not needs to be determined in a targeted manner on the premise that the interface is kept consistent on the basis of the density of the current image, so that stable performance of all dense pictures is ensured.
The step (3) mainly comprises two key steps, namely single-pixel reading and area reading, wherein the whole area reading step is as follows:
(1) initializing an output image C according to the output aperture, and setting the default initialized pixel value as 0;
(2) searching in the CSR structure according to row units, and determining the first position of valuable data in the row information in the array A;
(3) in the binary array, all data are positioned according to the first position and are sequentially written into the image C.
In the process of single-pixel reading, firstly, the position interval of the row information positioning data in the sparse matrix is utilized, and the characteristic that the column information is monotonically increased in the interval is utilized to perform binary search on the column information to complete the positioning of the single-point data.
Compared with the common traversing positioning scheme, the data positioning is carried out through the dichotomy, the time complexity can be reduced from O (N) to O (logN), wherein N is the column number of the sparse matrix, and the indexing performance is greatly improved.
Since the sparse matrix for compressing the sparse rows is data-stored in row units, it is also performed in row units when the image output is performed. Firstly, the positions of the head and the tail of the row in the whole sparse matrix are determined in a single-point positioning mode, and all data in the extraction area are extracted by means of the characteristics of continuous data to finish row data extraction. The method can reduce the time complexity from the original O (Nn2) to O (N2), wherein N is the size of the output image and N is the number of columns of the sparse matrix.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, for the image data acquired by the dark field imaging system, a Compression Sparse Row (CSR) mode is used for data compression, so that the data structure is flexible enough on the premise of ensuring a certain compression rate; the sparse matrix can fully utilize the characteristic of extremely low density, and a targeted structure is designed to omit the storage of 0 element, so that the purpose of optimizing the performance in time or space is achieved.
Drawings
FIG. 1 is a flowchart of the operation of the dark field image storage method deployed in a software system according to the present invention;
FIG. 2 is a schematic diagram of a dark field imaging system in the present invention, wherein (a) is a structural diagram of the dark field scattering imaging system, (b) is a scattering schematic diagram, and (c) is an exemplary diagram of a dark field diagram;
fig. 3 is a definition diagram of the density, in which (a) represents a dense picture and (b) represents a sparse picture;
FIG. 4 is a diagram of a CSR compression algorithm in an embodiment of the present invention;
FIG. 5 is a flow chart of binary search according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a line-scanned picture according to an embodiment of the present invention, in which (a) is a schematic diagram of line-scanned picture acquisition and (b) is a flowchart of line-scanned picture acquisition;
FIG. 7 is a density distribution diagram in an embodiment of the present invention.
Detailed Description
The invention will be described in further detail below with reference to the drawings and examples, which are intended to facilitate the understanding of the invention without limiting it in any way.
As shown in fig. 1, the method of the present invention is deployed in a software system, and data collected by an optical system and a hardware system finally needs to be summarized, processed and interacted with a user through a user interface. The whole software system processing flow is mainly divided into two stages, namely a data generation stage and a data display stage. The global image runs through the service cycle of the whole software system, stores all image information and is the main body of memory consumption. In order to reduce memory usage, in the "global image update" step, the global data is updated using eight-bit images. In order to ensure complete preservation and processing of information, 16-bit depth maps are used in the steps of image acquisition and data processing, and original data are stored in a warehouse for inspection in the step of image storage. The data display stage belongs to a user interaction stage, the intuitive feeling of a user on the system comes from the stage, the core of the stage is the interaction with a global image, and the reading efficiency of the stage is an important index influencing the response speed of the system in the stage.
The method of the present invention is mainly divided into three steps, wherein step 1 and step 2 occur in the data generation phase, and step 3 occurs in the data display phase.
(2-1) traversing the test picture set, and determining a gray threshold t;
(2-2) initializing a result of the compressed sparse row CSR, wherein the result comprises an array A and a binary array B;
(2-3) traversing original image data acquired by the dark field imaging system, and if the corresponding pixel value is greater than t, marking the pixel as valuable;
(2-4) extracting an (x, value) value of the valuable pixel (wherein x represents the number of columns of the corresponding pixel, and value represents the gray value of the corresponding pixel), and storing the value into a binary array B;
and (2-5) extracting the y value (representing the number of rows of the corresponding pixel points) of the first valuable pixel in each row, and storing the y value into the array A.
The definition of sparsity is shown in fig. 3, where (a) and (b) are dense pictures and sparse pictures, respectively. In a matrix, the total number of elements other than 0 is compared to the total number of all elements, called the densityhere. If the number of the non-zero elements accounts for most of the total number, the matrix is called as a dense matrix, and if the number of the zero elements is far larger than that of the non-zero elements, the matrix is called as a sparse matrix, pictures generated by the dark field imaging system all use black as a background, and clustered white spots representing defects or damages can appear at few positions. Therefore, the dark-field scatter picture fits perfectly with the definition of sparse matrix. The sparse matrix can fully utilize the characteristic of extremely low density, and a targeted structure is designed to omit the storage of 0 element, so that the purpose of optimizing the performance in time or space is achieved.
The present invention uses a sparse matrix structure of Compressed Sparse Rows (CSR), the compression algorithm of which is shown in fig. 4.
And 3, extracting data in line units, and completing the extraction of the whole line of data by searching and positioning the positions of head and tail elements of the single line of data so as to complete the final output of the whole image, wherein the steps are as follows:
(1) initializing an output image C according to the output aperture, and setting the default initialized pixel value as 0;
(2) searching in the CSR structure according to row units, and determining the first position of valuable data in the row information in the array A;
(3) in the binary array, all data are positioned according to the first position and are sequentially written into the image C.
In the ordered array, the time complexity can be reduced by using a binary search method, and the time complexity of the binary search is o (logn), which is very excellent time performance. The (x, value) binary vector in the CSR structure is stored in the order from top to bottom and from left to right. Although not totally ordered, x is incremented in a single line of data, so a binary search can be introduced, the flow chart for which is shown in fig. 5. The method fully utilizes the local order characteristic of the binary array, and reduces the time complexity to O (logN) by introducing binary search, wherein N is the number of matrix columns. The optimized algorithm has very slow time increase along with the increase of the number of picture columns, and compared with the time complexity of O (N) before optimization, O (logN) after optimization has obvious time advantage.
Since the CSR structure is based on sparse row compression, the access efficiency can be optimized in row units, specifically as shown in fig. 6, where (a) is a schematic diagram of the line-scan picture acquisition and (b) is a flowchart of the line-scan picture acquisition. The method makes full use of the characteristic of CSR, and can complete the positioning of the whole line of data by determining the head and tail positions because the whole line of data is continuously stored in the line scanning process.
To verify the effect of the present invention, an example of the application of the present invention to dark field imaging of a large aperture optical element is described below.
According to the steps described in the concrete implementation method, dark field scanning imaging is carried out on the optical element with the aperture of 800 multiplied by 800mm, and 10000 sub-aperture pictures are finally obtained in step 1.
In the step 2, the data conditions of different consistencies are distinguished and counted, so that sparse matrix compression is carried out according to the conditions. The data are classified into three grades of 0-0.01, 0.01-0.1 and 0.1-0.3 according to the density, and 200 pictures are randomly extracted from each grade to form three groups of data sets, wherein the density distribution is shown in fig. 7. According to the test data set, over 95.5% of the densities of the pictures belong to the low density group, so the overall temporal and spatial performance mainly depends on the low density group. The medium consistency group corresponds to the presence of normal size surface defects, etc., which account for less than 5%. The high density group corresponds to some abnormal conditions such as overlarge defect size, image overexposure and the like as shown in the figure, and the statistical distribution of the abnormal conditions does not show a monotonous decreasing trend any more.
In step 3, the performance of the CSR structure and COO structure compared to the native data structure is statistically tested, and the results are shown in table 1:
TABLE 1 summary of the results
Experiments prove that compared with an original image structure, the dark field image storage scheme designed by the invention has the advantages that the space occupation is reduced by 99.52%, the single-pixel reading time is reduced by 98.37%, and the block reading time is reduced by 99.12% on 95.5% of experimental data. Meanwhile, along with the increase of the density, the performance is kept relatively stable and is always superior to the original structure.
The embodiments described above are intended to illustrate the technical solutions and advantages of the present invention, and it should be understood that the above-mentioned embodiments are only specific embodiments of the present invention, and are not intended to limit the present invention, and any modifications, additions and equivalents made within the scope of the principles of the present invention should be included in the scope of the present invention.
Claims (6)
1. A dark field image storage method utilizing sparse matrix characteristics is characterized by comprising the following steps:
(1) scanning and imaging the sample to be detected by utilizing a dark field imaging system;
the dark field imaging system comprises: the device comprises a three-axis displacement table for placing a sample to be measured, a light source module for providing oblique illumination light for the sample to be measured, an imaging lens group and a CCD camera, wherein the imaging lens group is positioned right above the sample to be measured and used for receiving scattered light; a dark field image formed by the CCD camera is transmitted to a computer for processing, and the computer is also connected with the three-axis displacement table and used for controlling the accurate movement of the three-axis displacement table;
(2) selectively compressing the original image data acquired by the dark field imaging system by using a sparse matrix structure;
(3) and extracting data in line units, and finishing the positions of head and tail elements of single-line data by searching and positioning so as to finish the extraction of the whole line of data and further finish the final output of the whole image.
2. The dark field image storage method using the sparse matrix characteristic as claimed in claim 1, wherein in the step (1), the specific process of scanning and imaging the sample to be tested by using the dark field imaging system is as follows:
a sample to be detected is placed on a three-axis displacement table, and the three-axis displacement table enables the sample to accurately move in three directions, so that the system can be ensured to scan in a large range;
the light source module obliquely enters the system at a certain angle to illuminate the sample to be measured, and dark field scattering imaging is realized in an oblique illumination mode: when the sample is free of defects, the illumination ray A will be reflected at the sample surface and leave the system along the path A'; when incident light irradiates a defect, strong scattered light A' is generated on the surface of a sample, a part of light is captured by a photosensitive area of a CCD camera through an imaging lens group, and cluster white points are formed on an image;
finally, the image shot by the CCD camera is a dark field scattering image with a large-range black background and clustered white spots of different sizes distributed unevenly.
3. The dark-field image storage method using sparse matrix characteristics as claimed in claim 1, wherein in step (2), data compression is performed by compressing sparse row CSR, and the specific process is as follows:
(2-1) traversing the test picture set, and determining a gray threshold t;
(2-2) initializing a result of the compressed sparse row CSR, wherein the result comprises an array A and a binary array B;
(2-3) traversing original image data acquired by the dark field imaging system, and if the corresponding pixel value is greater than t, marking the pixel as valuable;
(2-4) extracting (x, value) values of the valuable pixels and storing the values into a binary array B, wherein x represents the number of columns of the corresponding pixel points, and value represents the gray value of the corresponding pixel points; (2-5) extracting the y value of the first valuable pixel in each row, and storing the y value into the array A, wherein y represents the row number of the corresponding pixel point.
4. The dark-field image storage method using the sparse matrix characteristic as claimed in claim 3, wherein in the step (2), when the sparse matrix structure is used to selectively perform data compression, based on the density of the current image, on the premise of keeping the interface consistent with the outside, it is determined in a targeted manner whether to perform data compression, so as to ensure that all the pictures with the density have stable performance.
5. The dark-field image storage method using sparse matrix characteristics as claimed in claim 3, wherein step (3) mainly comprises two key steps, namely single pixel reading and region reading, wherein the overall region reading step is as follows:
(1) initializing an output image C according to the output aperture, and setting the default initialized pixel value as 0;
(2) searching in the CSR structure according to row units, and determining the first position of valuable data in the row information in the array A;
(3) in the binary array, all data are positioned according to the first position and are sequentially written into the image C.
6. The dark field image storage method using the sparse matrix characteristic as claimed in claim 1, wherein in the step (3), during the single pixel reading process, firstly, the position interval of the data in the sparse matrix is located by using the row information, and in the interval, the column information is subjected to binary search by using the characteristic that the column information is monotonically increased to complete the location of the single point data.
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| CN102891956A (en) * | 2012-09-25 | 2013-01-23 | 北京理工大学 | Method for designing compression imaging system based on coded aperture lens array |
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| US20190056333A1 (en) * | 2017-08-16 | 2019-02-21 | Siemens Energy, Inc. | Method and system for detecting defects on surface of object |
| CN112860818A (en) * | 2021-03-17 | 2021-05-28 | 吉林大学 | Sparse matrix compression storage method for dictionary nested dictionary data structure |
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| CN102891956A (en) * | 2012-09-25 | 2013-01-23 | 北京理工大学 | Method for designing compression imaging system based on coded aperture lens array |
| CN103472456A (en) * | 2013-09-13 | 2013-12-25 | 中国科学院空间科学与应用研究中心 | Active imaging system and method based on sparse aperture compressing calculation correlation |
| CN103471718A (en) * | 2013-09-13 | 2013-12-25 | 中国科学院空间科学与应用研究中心 | Hyperspectral imaging system and method based on sparse aperture compressing calculation correlation |
| US20190056333A1 (en) * | 2017-08-16 | 2019-02-21 | Siemens Energy, Inc. | Method and system for detecting defects on surface of object |
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