CN116503372A - Spectrogram noise detection method for spectrometer - Google Patents
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Abstract
The invention discloses a spectrogram noise detection method for a spectrometer, and belongs to the technical field of spectral information processing. According to the invention, the uniform image block of the spectrometer is obtained based on the image entropy value, so that the entropy standard judging efficiency of the uniform image block can be improved, the evaluation of mixed noise can be realized based on the noise vector judgment of the uniform image block after the acquisition efficiency of the uniform image block is improved, the detection of noise abnormality of a spectrogram can be quickly carried out, the detection performance and the processing effect can be improved, the detection precision of abnormal noise can be improved after the filtering processing and the evaluation of the image block before the detection, the evaluation selection efficiency of the image block can be improved through the entropy fuzzy judgment of the uniform image block, and the detection efficiency can be improved.
Description
Technical Field
The invention belongs to the technical field of spectrum information processing, and particularly relates to a spectrogram noise detection method for a spectrometer.
Background
The hyperspectral imager is a remote sensing image instrument prepared by combining the patterns and features, has good spectrum resolution, has great advantages in the aspects of target identification and recognition compared with the multispectral imager, has more signal noise in the hyperspectral image, and is easy to generate noise in the acquisition process, thereby influencing the recognition of the ground object space information. Noise removal is a necessary step in hyperspectral image processing, so how to detect anomalies in a spectrogram, which do not conform to a background image, is a key of noise processing, and existing detection of spectrogram noise generally processes noise in an image through a filtering algorithm, and through designing a corresponding threshold value, an abnormal image exceeding the threshold value in the image is judged, and then noise of the spectrogram image is obtained.
Chinese patent publication No. CN100334467C discloses a method for processing hyperspectral image of domestic hyperspectral remote sensor, which can detect and remove noise in hyperspectral image spectral domain, and belongs to the field of application of remote sensing technology in engineering science and technology. The method is characterized in that the detection and discrimination of the hyperspectral image spectral domain noise or the fine spectral characteristics and the removal of the larger spectral domain noise are realized through a variable suitable for spectral noise evaluation, a condition suitable for spectral noise evaluation judgment and a method for finding a filter suitable for the hyperspectral image spectral domain; mainly solves the technical problems of how to find out the method and system software and related hardware and the like applicable to the hyperspectral image spectral domain filter. According to the scheme, noise in a spectrum can be effectively removed through the arrangement of the filter, and most of original spectrum characteristics of the spectrum can be reserved, but when the spectrum image is actually used, variable measurement of a spectrum image is difficult, thresholds of remote sensing images in different areas can be changed, so that the problem of low denoising efficiency exists, and an improvement space exists.
Disclosure of Invention
The invention aims at: in order to solve the problems that the variable measurement of a spectrum image is difficult, the thresholds of remote sensing images in different areas can be changed, and the denoising efficiency is low, the spectrogram noise detection method for the spectrometer is provided.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the spectrogram noise detection method for the spectrometer specifically comprises the following steps of:
s101, acquiring a plurality of noise spectrum images with spectrogram noise output by a spectrometer through sample pre-acquisition, screening images with prediction conditions after preprocessing and screening the spectrogram images, and obtaining a prediction image;
s102, after the obtained predicted image is subjected to image division, uniform image blocks are selected, entropy analysis is performed on the image blocks cut by the obtained spectrogram through an entropy vector analysis method, and uniform image blocks serving as reference blocks are selected from the average entropy values of the image blocks and used for the region serving as filtering uniform image blocks without characteristics;
s103, based on the uniform image entropy value of the reference block, comparing and judging the characteristics of the rest image blocks, performing pixel point mixed noise evaluation on the selected image through multiple linear regression to obtain mixed noise residual errors, and separating target spectrums and noise through pixel bands of the uniform image blocks of each wave band to obtain mixed noise variance of any uniform image block in any wave band;
s104, denoising the spectrum image of the target image block based on the obtained mixed noise variance to obtain a denoised spectrum image, and taking the denoised spectrum image as a noise vector standard image;
s105, detecting image noise, namely taking the noise vector standard image as a vector of the spectral image to be predicted after denoising, and obtaining a spectrogram noise detection result after carrying out abnormal comparison detection on the noise vector standard image and the spectral image to be detected.
As a further description of the above technical solution:
the method for evaluating the mixed noise specifically comprises the following steps: and carrying out vector evaluation of mixed noise by using image space information through multiple linear regression based on a uniform background image block in the average image of the spectrogram without spectral characteristics, and carrying out target spectrum and noise separation on the uniform image block and the pixel points of the band image to obtain the mixed noise variance based on the variation of the uniform background image block.
As a further description of the above technical solution:
the entropy value analysis step includes: and carrying out homogenization division on the spectrum image to obtain a plurality of image blocks, carrying out standard deviation calculation on the standard deviation value of each image block and the discrete position of image pixel distribution to obtain entropy values, carrying out descending order arrangement on all the entropy values, and carrying out matching denoising on the standard deviation value corresponding to the entropy values to obtain the nearest image block which is a uniform image block.
As a further description of the above technical solution:
the image dividing size is divided equally based on the spectrum image size, and the side length of the divided image block is the side length of the spectrum image。
As a further description of the above technical solution:
the uniform image block is an average image which does not contain image features and is used for carrying out vector analysis on the image block containing the features through the average image.
As a further description of the above technical solution:
the method for evaluating the mixed noise comprises the step of performing multiple regression analysis on the selected uniform image blocks to obtain the evaluated mixed noise.
As a further description of the above technical solution:
the preprocessing of the spectrum image comprises screening and removing the spectrum image which does not have judgment conditions based on the visualization processing after the spectrum image is generated.
As a further description of the above technical solution:
the visualization processing method comprises the steps of carrying out superposition comparison on the basis of multiple images, obtaining high-frequency characteristic information of the images on the basis of coordinates of main body contours of original images, removing low-frequency components of the high-frequency characteristic information on the basis of the original information of the multiple images, reserving main body edge high-frequency information for visual observation in the images, obtaining overall contours of noise of each object after the main body edge high-frequency information is used, carrying out filtering operation on the removed low-frequency components, carrying out visual judgment on the main body edge images which are most attached to main body noise after the low-frequency component information is averaged, and obtaining spectral image information with judgment conditions after preprocessing on the basis of a visual threshold structure.
As a further description of the above technical solution:
and the overlapping quantity of the multiple images to be compared in the overlapping comparison of the multiple images is 5-10 frames.
As a further description of the above technical solution:
a computer device comprising a processor and a memory having stored therein at least one instruction that is loaded and executed by the processor to implement the operations performed by the spectrogram noise detection method for a spectrometer of any one of claims 1-9.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
according to the invention, the uniform image block of the spectrometer is obtained based on the image entropy value, so that the entropy standard judging efficiency of the uniform image block can be improved, the evaluation of mixed noise can be realized based on the noise vector judgment of the uniform image block after the acquisition efficiency of the uniform image block is improved, the detection of noise abnormality of a spectrogram can be quickly carried out, the detection performance and the processing effect can be improved, the detection precision of abnormal noise can be improved after the filtering processing and the evaluation of the image block before the detection, the evaluation selection efficiency of the image block can be improved through the entropy fuzzy judgment of the uniform image block, and the detection efficiency can be improved.
Drawings
Fig. 1 is a schematic diagram of the overall structure of a spectrogram noise detection method for a spectrometer according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
To implement the following embodiments, a computer device should be designed, including a processor and a memory, where at least one instruction is stored, and the processor loads and executes operations performed by a spectrogram noise detection method for a spectrometer;
example 1
Referring to fig. 1, the present invention provides a technical solution: the spectrogram noise detection method for the spectrometer specifically comprises the following steps of:
s101, acquiring a plurality of noise spectrum images with spectrogram noise output by a spectrometer through sample pre-acquisition, screening images with prediction conditions after preprocessing and screening the spectrogram images, and obtaining a prediction image;
s102, after the obtained predicted image is subjected to image division, uniform image blocks are selected, entropy analysis is performed on the image blocks cut by the obtained spectrogram through an entropy vector analysis method, and uniform image blocks serving as reference blocks are selected from the average entropy values of the image blocks and used for the region serving as filtering uniform image blocks without characteristics;
s103, based on the uniform image entropy value of the reference block, comparing and judging the characteristics of the rest image blocks, performing pixel point mixed noise evaluation on the selected image through multiple linear regression to obtain mixed noise residual errors, and separating target spectrums and noise through pixel bands of the uniform image blocks of each wave band to obtain mixed noise variance of any uniform image block in any wave band;
s104, denoising the spectrum image of the target image block based on the obtained mixed noise variance to obtain a denoised spectrum image, and taking the denoised spectrum image as a noise vector standard image;
s105, detecting image noise, namely taking the noise vector standard image as a vector of the spectral image to be predicted after denoising, and obtaining a detection result of the spectrogram noise after carrying out abnormal comparison detection on the noise vector standard image and the spectral image to be detected;
the method comprises the steps of carrying out abnormal comparison on a spectrum image to be detected, specifically comprising the steps of carrying out comparison detection through an RX algorithm, specifically comprising the steps of setting image data to obey normal distribution which is different in homogeneity but is intended to be listened to by a covariance matrix under the condition that a target and a background are unknown, and establishing binary hypothesis:
wherein: h0 represents the absence of a target; h1 represents the presence of a target; x is the spectral vector to be detected; n represents a noise vector; s represents a target spectral vector; a is a coefficient, when a=0, the assumption H is satisfied 0 When a > 0, satisfy hypothesis H 1 。
Based on the basis of binary assumptions, the following mahalanobis distance detection operator is defined:
；
wherein:is the background mean value; c is background covariance matrix; />Is a discrimination threshold; /> Indicating when->Greater than or equal to->When, the assumption H1 is satisfied, when +.>When the pixel point is smaller than or equal to the threshold value, the assumption H0 is satisfied, the RX algorithm uses an operator to detect, and when a certain pixel point is +.>When the value exceeds the judgment threshold value, the abnormal pixel is considered, and after the point-by-point detection, a detection result can be obtained;
based on the obtained spectrum vector to be detected, the spectrum vector to be detected can be brought into an RX operator to obtain a detection result
The mixed noise evaluation method specifically comprises the following steps: based on a uniform background image block in a spectrogram average image which does not contain spectral features, performing multiple linear regression, performing vector evaluation of mixed noise by using image space information, and performing target spectrum and noise separation judgment on the uniform image block and pixel points of a wave band image to be analyzed to obtain a mixed noise variance based on the variation of the uniform background image block;
the entropy value analysis step includes: carrying out homogenization division on the spectrum image to obtain a plurality of image blocks, carrying out standard deviation calculation, calculating the standard deviation value of each image block and the discrete position of image pixel distribution to obtain entropy values, carrying out descending order arrangement on all the entropy values, carrying out matching denoising on the standard deviation value corresponding to the entropy values, and obtaining the nearest image block as a uniform image block;
the image dividing size is equally divided based on the spectrum image size, and the side length of the divided image block is the side length of the spectrum image；
The uniform image block is an average image which does not contain image features and is used for carrying out vector analysis on the image block containing the features through the average image, and further, the uniform image block is a region with less textures and edges and more uniformity in the image, and the uniform image block can be considered to contain no abnormal target to be detected;
after the image is grayed, the same normalization processing is carried out on the image standard variance value and the vector of the pixel distribution discrete position, wherein the image standard variance value is also subjected to the screening processing of the variance value through an image quality evaluation matrix;
the method specifically further comprises the step of outputting a background mean value in a spectrogram noise detection result as a filtering threshold value of noise processing, and the background mean value is used for denoising the spectrogram noise;
example 2
The spectrogram noise detection method for the spectrometer specifically comprises the following steps of:
s101, acquiring a plurality of noise spectrum images with spectrogram noise output by a spectrometer through sample pre-acquisition, screening images with prediction conditions after preprocessing and screening the spectrogram images, and obtaining a prediction image;
s102, after the obtained predicted image is subjected to image division, uniform image blocks are selected, entropy analysis is performed on the image blocks cut by the obtained spectrogram through an entropy vector analysis method, and uniform image blocks serving as reference blocks are selected from the average entropy values of the image blocks and used for the region serving as filtering uniform image blocks without characteristics;
s103, based on the uniform image entropy value of the reference block, comparing and judging the characteristics of the rest image blocks, performing pixel point mixed noise evaluation on the selected image through multiple linear regression to obtain mixed noise residual errors, and separating target spectrums and noise through pixel bands of the uniform image blocks of each wave band to obtain mixed noise variance of any uniform image block in any wave band;
s104, denoising the spectrum image of the target image block based on the obtained mixed noise variance to obtain a denoised spectrum image, and taking the denoised spectrum image as a noise vector standard image;
s105, detecting image noise, namely taking the noise vector standard image as a vector of the spectral image to be predicted after denoising, and obtaining a detection result of the spectrogram noise after carrying out abnormal comparison detection on the noise vector standard image and the spectral image to be detected;
the mixed noise evaluation method specifically comprises the following steps: based on a uniform background image block in a spectrogram average image which does not contain spectral features, performing multiple linear regression, performing vector evaluation of mixed noise by using image space information, performing target spectrum and noise separation judgment on the uniform image block and pixel points of a wave band image to be analyzed, and obtaining a mixed noise variance based on the variation of the uniform background image block, wherein the entropy value analysis step comprises the following steps: carrying out homogenization division on the spectrum image to obtain a plurality of image blocks, carrying out standard deviation calculation, calculating the standard deviation value of each image block and the discrete position of image pixel distribution to obtain entropy values, carrying out descending order arrangement on all the entropy values, carrying out matching denoising on the standard deviation value corresponding to the entropy values, and obtaining the nearest image block as a uniform image block;
the image dividing size is equally divided based on the spectrum image size, and the side length of the divided image block is the side length of the spectrum imageThe method comprises the steps that a uniform image block is an average image which does not contain image features, is used for carrying out vector analysis on the image block containing the features through the average image, and further comprises calculation of discrete positions of image pixel distribution of the uniform image block and a spectrum image;
the calculation of entropy obtained by the standard variance value of the image block and the discrete position of the image pixel distribution is specifically as follows: after the input noise image I is grayed, calculating a standard deviation and a discrete position value of a blocking area to obtain an entropy value, wherein the variance value and the discrete position value standard based on the entropy value in the optimal condition are 0, and compared with a single variance value scale, searching a smooth block is more accurate and effective, in statistics, the variance of a random variable describes the discrete degree, namely the distance of the variable from an expected value, and is the most important method for measuring and calculating the discrete degree of numerical data; in contrast, the smaller the size of the image, the smoother the image block, the smaller the variance of the image block, and the more complex the texture is contained in the image block, the larger the variance of the image block, so that the smoothness of the image block can be measured by the size of the variance value of the image block;
the variance is calculated as:；
in the method, in the process of the invention,is the standard deviation value>Representing pixel values for points within a certain image block in the image,an average of the pixel values within the block;
example 3
The spectrogram noise detection method for the spectrometer specifically comprises the following steps of:
s101, acquiring a plurality of noise spectrum images with spectrogram noise output by a spectrometer through sample pre-acquisition, screening images with prediction conditions after preprocessing and screening the spectrogram images, and obtaining a prediction image;
s102, after the obtained predicted image is subjected to image division, uniform image blocks are selected, entropy analysis is performed on the image blocks cut by the obtained spectrogram through an entropy vector analysis method, and uniform image blocks serving as reference blocks are selected from the average entropy values of the image blocks and used for the region serving as filtering uniform image blocks without characteristics;
s103, based on the uniform image entropy value of the reference block, comparing and judging the characteristics of the rest image blocks, performing pixel point mixed noise evaluation on the selected image through multiple linear regression to obtain mixed noise residual errors, and separating target spectrums and noise through pixel bands of the uniform image blocks of each wave band to obtain mixed noise variance of any uniform image block in any wave band;
s104, denoising the spectrum image of the target image block based on the obtained mixed noise variance to obtain a denoised spectrum image, and taking the denoised spectrum image as a noise vector standard image;
s105, detecting image noise, namely taking the noise vector standard image as a vector of the spectral image to be predicted after denoising, and obtaining a detection result of the spectrogram noise after carrying out abnormal comparison detection on the noise vector standard image and the spectral image to be detected;
the mixed noise evaluation method specifically comprises the following steps: based on a uniform background image block in a spectrogram average image which does not contain spectral features, performing multiple linear regression, performing vector evaluation of mixed noise by using image space information, performing target spectrum and noise separation judgment on the uniform image block and pixel points of a wave band image to be analyzed, and obtaining a mixed noise variance based on the variation of the uniform background image block, wherein the entropy value analysis step comprises the following steps: carrying out homogenization division on a spectrum image to obtain a plurality of image blocks, carrying out standard deviation calculation, calculating standard deviation values of each image block and discrete positions of image pixel distribution to obtain entropy values, carrying out descending order arrangement on all the entropy values, carrying out matching denoising on the standard deviation values corresponding to the entropy values to obtain the nearest image block which is a uniform image block, carrying out equal division on the image division size based on the spectrum image size, and taking the side length of the divided image block as the side length of the spectrum imageA uniform image block is an average image that does not contain image features, for passing an average image over a map containing featuresThe method comprises the steps of carrying out vector analysis on an image block, calculating discrete positions of image pixel distribution of a uniform image block and a spectrum image, specifically carrying out the same normalization processing on the basis of a variance value and a vector of the discrete position of the pixel distribution after the image is grayed, wherein the standard variance value of the image block is also subjected to the screening processing of the variance value through an image quality evaluation matrix, and the preprocessing of the spectrum image comprises the step of screening and removing the spectrum image which does not have judgment conditions based on the visualization processing after the spectrum image is generated;
further, the visualization processing method comprises the steps of performing superposition contrast on the basis of multiple images, obtaining high-frequency characteristic information of the images on the basis of coordinates of main body contours of original images, removing low-frequency components of the high-frequency characteristic information on the basis of the original information of the multiple images, reserving main body edge high-frequency information for visual observation in the images, obtaining overall contours of noise of each object after the main body edge high-frequency information is used, performing filtering operation on the removed low-frequency components, performing visualization judgment on the main body edge images which are most attached to main body noise after the low-frequency component information is averaged, obtaining spectrum image information with judgment conditions after preprocessing on the basis of a visualization threshold structure, and enabling the superposition quantity of the multiple images to be compared in superposition contrast of the multiple images to be 5-10 frames.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (10)
1. The spectrogram noise detection method for the spectrometer is characterized by comprising the following steps of:
s101, acquiring a plurality of noise spectrum images with spectrogram noise output by a spectrometer through sample pre-acquisition, screening images with prediction conditions after preprocessing and screening the spectrogram images, and obtaining a prediction image;
s102, after the obtained predicted image is subjected to image division, uniform image blocks are selected, entropy analysis is performed on the image blocks cut by the obtained spectrogram through an entropy vector analysis method, and uniform image blocks serving as reference blocks are selected from the average entropy values of the image blocks and used for the region serving as filtering uniform image blocks without characteristics;
s103, based on the uniform image entropy value of the reference block, comparing and judging the characteristics of the rest image blocks, performing pixel point mixed noise evaluation on the selected image through multiple linear regression to obtain mixed noise residual errors, and separating target spectrums and noise through pixel bands of the uniform image blocks of each wave band to obtain mixed noise variance of any uniform image block in any wave band;
s104, denoising the spectrum image of the target image block based on the obtained mixed noise variance to obtain a denoised spectrum image, and taking the denoised spectrum image as a noise vector standard image;
s105, detecting image noise, namely taking the noise vector standard image as a vector of the spectral image to be predicted after denoising, and obtaining a spectrogram noise detection result after carrying out abnormal comparison detection on the noise vector standard image and the spectral image to be detected.
2. The spectrogram noise detection method for a spectrometer according to claim 1, wherein the mixed noise evaluation method specifically comprises: and carrying out vector evaluation of mixed noise by using image space information on the basis of uniform background image blocks in the average image of the spectrogram which does not contain spectral features, and carrying out target spectrum and noise separation judgment on the uniform image blocks and pixel points of the image of the wave band to be analyzed to obtain the mixed noise variance based on the variation of the uniform background image blocks.
3. The spectrogram noise detection method for a spectrometer according to claim 2, wherein the entropy analysis step comprises: and carrying out homogenization division on the spectrum image to obtain a plurality of image blocks, carrying out standard deviation calculation on the standard deviation value of each image block and the discrete position of image pixel distribution to obtain entropy values, carrying out descending order arrangement on all the entropy values, and carrying out matching denoising on the standard deviation value corresponding to the entropy values to obtain the nearest image block which is a uniform image block.
4. The method according to claim 3, wherein the image division size is divided equally based on the spectral image size, and the side length of the divided image block is the side length of the spectral image。
5. The method according to claim 2, wherein the uniform image block is an average image containing no image features, and the vector analysis is performed on the image block containing the features by the average image.
6. The spectrogram noise detection method for a spectrometer according to claim 3, further comprising calculating discrete positions of image pixel distribution of the uniform image block and the spectral image, specifically by subjecting the image to the same normalization process based on the variance value and the vector of the discrete positions of the pixel distribution after the image is grayed, wherein the image block standard variance value is further subjected to the variance value screening process by the image quality evaluation matrix.
7. The method according to claim 1, wherein the preprocessing of the spectral image includes screening out the spectral image without judgment condition based on the visualization processing after the spectral image is generated.
8. The spectrogram noise detection method for a spectrometer according to claim 7, wherein the visualization processing method comprises the steps of performing superposition comparison based on multiple images, obtaining high-frequency characteristic information of images based on coordinates of main body contours of original images, removing low-frequency components of the high-frequency characteristic information based on the original information of the multiple images, retaining main body edge high-frequency information for visual observation in the images, obtaining overall contours of noise of each object based on the main body edge high-frequency information, performing filtering operation on the removed low-frequency components, performing visual judgment on the main body edge images which are most attached to the main body noise after the low-frequency component information is averaged, and obtaining the spectral image information with judgment conditions after preprocessing based on a visual threshold structure.
9. The method for detecting noise of spectrograms of claim 8, wherein the number of superimposed images to be compared in the superimposed contrast of the multiple images is 5-10 frames.
10. A computer device comprising a processor and a memory having stored therein at least one instruction that is loaded and executed by the processor to implement the operations performed by the spectrogram noise detection method for a spectrometer of any one of claims 1-9.
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| CN118072254A (en) * | 2024-04-18 | 2024-05-24 | 辽宁通安消防安全技术工程有限公司 | Intelligent detection method and system for instantaneous explosion open fire |
| CN118072254B (en) * | 2024-04-18 | 2024-09-03 | 辽宁通安消防安全技术工程有限公司 | Intelligent detection method and system for instantaneous explosion open fire |
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