CN113222853B - Progressive infrared image noise reduction method based on noise estimation - Google Patents
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Abstract
The invention provides a progressive infrared image noise reduction method based on noise estimation, which realizes a noise weight matrix of a noise-containing infrared image through a bilateral filtering algorithm principle of a spatial domain, realizes gradient estimation of noise energy in the noise-containing infrared image through frequency domain filtering of gray difference of neighborhood pixels and central pixels, realizes progressive infrared image noise reduction through setting the step length of the progressive filtering, and realizes an iterative self-adaptive progressive infrared image noise reduction algorithm through estimating the noise level of the infrared image. The invention has the beneficial effects that: the noise level of the infrared image is reduced, and the visual quality of the infrared image is improved.
Description
Technical Field
The invention relates to the field of infrared image processing, in particular to a progressive infrared image denoising method based on noise estimation.
Background
Noise reduction, i.e. the reconstruction of an original image from a noise-contaminated image, is an important issue in image processing. On one hand, the infrared image has strong noise and is limited by the process development level of the infrared focal plane, and the infrared image has non-uniformity; on the other hand, electronic thermal noise in the infrared imaging system also affects the infrared image. The noise model in the infrared image is generally assumed to be additive white gaussian noise.
The most advanced image noise reduction algorithms at present are block-based noise reduction algorithms. A common method is to find non-local similar blocks within a neighborhood window and then normalize and simultaneously denoise, such as non-local mean (NLM) or the like. The best to achieve noise reduction in this respect is the block matching and 3D filtering (BM3D) algorithm, BM3D stacks the matched patches together to reduce noise by 3D wavelet transform. An improved version of BM3D is the BM3D-SAPCA algorithm. However, they all have very large algorithm complexity without exception, and are not suitable for systems with high real-time requirements. On the other hand, the dual-domain image noise reduction (DDID) is proved to be a simple noise reduction algorithm capable of realizing a high-quality noise reduction effect, the algorithm comprehensively considers the characteristics of a spatial domain and a frequency domain, the noise reduction of a large-amplitude noise-containing signal is realized through spatial domain filtering, and the noise reduction of a small-amplitude noise-containing signal is realized through the frequency domain. However, the implementation of the algorithm requires a guide image, which is often not satisfied in practical use scenarios.
Disclosure of Invention
In order to solve the problems, the invention provides a progressive infrared image noise reduction method based on noise estimation, which realizes a noise weight matrix of a noisy infrared image through a bilateral filtering algorithm principle of a spatial domain, realizes gradient estimation of noise energy in the noisy infrared image through frequency domain filtering of gray difference of neighborhood pixels and central pixels, realizes progressive infrared image noise reduction through setting step length of progressive filtering, and realizes an iterative self-adaptive progressive infrared image noise reduction algorithm through estimating the noise level of the infrared image.
A progressive infrared image noise reduction method based on noise estimation comprises the following steps:
s101: acquiring an infrared image containing noise, and estimating the noise level of the infrared image;
s102: continuously overlapping and blocking the infrared image containing the noise with the step length of 1 to obtain image blocks;
s103: taking the central pixel of the image block as an anchor point, calculating the distance of other pixel points in the neighborhood relative to the central anchor point, and calculating a Gaussian weight matrix G1 of the neighborhood pixel by taking the distance as an independent variable;
s104: taking the central pixel of the image block as an anchor point, calculating the difference between the gray value of other pixel points in the neighborhood and the gray value of the central anchor point, and calculating a Gaussian weight matrix G2 of the neighborhood pixel by taking the difference of the gray values as an independent variable;
s105: performing dot multiplication on G1 and G2 to obtain a Gaussian weight matrix G3 of a joint spatial domain and a gray scale domain;
s106: performing dot multiplication on the difference of the gray values and a Gaussian weight matrix G3 to obtain weighted image blocks;
s107: performing two-dimensional Fourier transform on the weighted image blocks to obtain a frequency spectrum matrix;
s108: calculating a Gaussian weight matrix G4 by taking the frequency spectrum matrix as an independent variable;
s109: weighting and summing the frequency spectrum matrix and the Gaussian weight matrix G4, and carrying out normalization processing to obtain noise energy gradient estimation of the central anchor point pixel of the image block;
s110: subtracting the lambda times of noise energy gradient estimation from the gray value of the central anchor point pixel of the image block to obtain the gray value of the central anchor point pixel of the image block after noise reduction, and further obtain the infrared image after noise reduction;
s111: and (3) performing iterative processing of the steps S102-S111 on the infrared image containing the noise, and ending the iterative process to obtain the final infrared image after noise reduction if the noise level difference of the infrared image after the next noise reduction is smaller than or equal to a preset threshold value T compared with the noise level of the infrared image after the previous noise reduction.
Further, step S101 is specifically: performing convolution on an infrared image containing noise through a kernel consisting of two Laplacian masks to obtain the noise level of the infrared image;
wherein, the nucleus composed of two Laplacian masks is shown as formula (1):
n is a nucleus of the composition;
the noise level is as in equation (2):
n is the noise level; w and H represent the width and height of the noise-containing infrared image; i denotes the infrared image containing noise.
Further, in step S102, the image blocking specifically includes: symmetrically copying and expanding the infrared image containing the noise in four directions of top, bottom, left and right according to the radius and the side length of the block to obtain a symmetrically copied and expanded infrared image containing the noise, and then partitioning by taking the pixel of the infrared image containing the noise as a central point and taking the step length as 1 pixel; the calculation formula of the side length and the radius of the block is shown as the formula (3):
r＝(a-1)/2 (3)
r denotes the radius of the block and a denotes the side length of the block.
Further, in step S103, the calculation formula of the distances between other pixel points in the neighborhood and the central anchor point is as shown in formula (4):
where x, y represent the coordinate position of the patch, r represents the radius of the patch, and s represents the distance of the (x, y) location pixel from the anchor point.
In step S103, the gaussian weight matrix G1 is represented by equation (5):
where G1 represents a Gaussian weight matrix of the neighborhood pixels with distance as argument, s represents the distance of the pixel to the anchor point, σsThe standard deviation of the gaussian function is represented.
Further, in step S104, the gaussian weight matrix G2 is expressed by equation (6):
where G2 represents a gaussian weight matrix of the neighborhood pixels with the difference in gray values as an argument, G represents the gray difference between the neighborhood pixels and the central anchor pixel, σgThe standard deviation of the gaussian function is represented.
Further, in step S108, the gaussian weight matrix G4 has the formula shown in formula (7):
where F denotes the spectral matrix of the weighted block, σfThe standard deviation of the gaussian function is indicated.
Further, in step S109, the noise energy gradient of the anchor pixel at the center of the image block is estimated as shown in equation (8):
gradient＝sum(|F|*G4) /sum(G4) (8)
where gradient represents the gradient estimate of the noise energy, F represents the spectral matrix of the weighted partitions, G4 represents the gaussian weight matrix of the spectral matrix of the weighted partitions, and sum represents the sum.
In step S110, the gray-scale value of the center anchor point pixel of the denoised image partition is as shown in formula (9):
pi＝pi-1-λ*gradient (9)
wherein p isiRepresenting the gray value, p, of the anchor pixel after the ith iteration noise reductioni-1And representing the gray value of the anchor point pixel after the i-1 th iteration noise reduction, wherein gradient represents the gradient estimation of the noise energy of the block anchor point pixel, and lambda represents the step length.
The beneficial effects provided by the invention are as follows: the noise level of the infrared image is reduced, and the visual quality of the infrared image is improved.
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FIG. 1 is a flow chart of a progressive infrared image denoising method based on noise estimation according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be further described with reference to the accompanying drawings.
Referring to fig. 1, a progressive infrared image denoising method based on noise estimation includes the following steps: .
S101: acquiring an infrared image containing noise, and estimating the noise level of the infrared image;
in step S101, the estimating a noise level of the infrared image containing noise specifically includes:
s201: performing convolution operation through a kernel composed of two Laplacian masks, wherein the two Laplacian masks are shown as formulas (1) and (2), and the kernel composed of the two Laplacian masks is shown as a formula (3);
s202: the noise level of the infrared image containing the noise can be estimated by performing convolution on the infrared image containing the noise by using a kernel composed of the two Laplacian masks, and the formula is shown as formula (4), wherein n represents the estimated noise level of the infrared image containing the noise, I represents the infrared image containing the noise, and W and H represent the width and height of the infrared image containing the noise.
S102: continuously overlapping and blocking the infrared image containing the noise with the step length of 1 to obtain image blocks;
in step S102, continuously overlapping blocks with a step length of 1 are performed on the infrared image containing noise, where the block side length is usually odd, specifically, in order to keep the image size unchanged, the infrared image containing noise is symmetrically copied and expanded in four directions, i.e., up, down, left, and right, according to the radius of the blocks, so as to obtain a symmetrically copied and expanded infrared image containing noise, and then the block is performed with the pixel of the infrared image containing noise as a center point and the step length of 1 pixel, where the calculation formulas of the block side length and the radius are as shown in formula (5):
r＝(a-1)/2 (5)
where r represents the radius of the block and a represents the side length of the block.
S103: taking the central pixel of the image block as an anchor point, calculating the distance of other pixel points in the neighborhood relative to the central anchor point, and calculating a Gaussian weight matrix G1 of the neighborhood pixel by taking the distance as an independent variable;
in step S103, the calculation formula of the distance between other pixel points in the neighborhood and the central anchor point is as follows (6):
where x, y represent the coordinate position of the patch, r represents the radius of the patch, and s represents the distance of the (x, y) location pixel from the anchor point.
In step S103, the gaussian weight matrix G1 is represented by equation (7):
where G1 represents a Gaussian weight matrix of the neighborhood pixels with distance as argument, s represents the distance of the pixel to the anchor point, σsRepresenting the standard deviation of a Gaussian function
S104: taking the central pixel of the image block as an anchor point, calculating the difference between the gray value of other pixel points in the neighborhood and the gray value of the central anchor point, and calculating a Gaussian weight matrix G2 of the neighborhood pixel by taking the difference of the gray values as an independent variable;
in step S104, the gaussian weight matrix G2 is expressed by equation (8):
wherein G2 represents a gaussian weight matrix of the neighborhood pixels with the difference of gray values as an argument, G represents the gray difference between the neighborhood pixels and the central anchor point pixel, σgThe standard deviation of the gaussian function is represented.
S105: performing dot multiplication on G1 and G2 to obtain a Gaussian weight matrix G3 of a joint spatial domain and a gray scale domain; the gaussian weight matrix G3 for the joint spatial domain and gray scale domain has the formula shown in equation (9):
G3＝G1*G2 (9)
s106: performing dot multiplication on the difference of the gray values and a Gaussian weight matrix G3 to obtain weighted image blocks; in step S106, the weighted image blocks have the formula shown in formula (10):
block＝g*G3 (10)
wherein block represents the weighted image blocks, G represents the gray difference between the block neighborhood pixels and the anchor pixels, and G3 represents the gaussian weight matrix of the joint spatial domain and the gray domain.
S107: performing two-dimensional Fourier transform on the weighted image blocks to obtain a frequency spectrum matrix;
in step S107, the weighted spectrum matrix of the image block has the formula shown in equation (11):
F＝fft2(block) (11)
where F represents a spectrum matrix, block represents the weighted partitions, and fft2 represents a two-dimensional fourier transform.
S108: calculating a Gaussian weight matrix G4 by taking the frequency spectrum matrix as an independent variable;
in step S108, the gaussian weight matrix G4 has the formula (12):
where F denotes the spectral matrix of the weighted block, σfThe standard deviation of the gaussian function is indicated.
S109: weighting and summing the frequency spectrum matrix and the Gaussian weight matrix G4, and carrying out normalization processing to obtain noise energy gradient estimation of the central anchor point pixel of the image block;
in step S109, the noise energy gradient estimation of the image block center anchor pixel is as shown in equation (13):
gradient＝sum(|F|*G4)/sum(G4) (13)
wherein gradient represents gradient estimation of noise energy, F represents a weighted spectrum matrix of the blocks, and | F | represents a modulus value; g4 denotes a gaussian weight matrix of the spectrum matrix of the weighted partitions, sum denotes summation.
S110: subtracting the lambda times of noise energy gradient estimation from the gray value of the central anchor point pixel of the image block to obtain the gray value of the central anchor point pixel of the image block after noise reduction, and further obtain the infrared image after noise reduction;
in step S110, the gray-scale value of the center anchor point pixel of the denoised image partition is as shown in formula (14):
pi＝pi-1-λ*gradient (14)
wherein p isiRepresenting the gray value p of the anchor pixel after the ith iteration noise reductioni-1And representing the gray value of the anchor point pixel after the i-1 th iteration noise reduction, wherein gradient represents the gradient estimation of the noise energy of the block anchor point pixel, and lambda represents the step length.
S111: and (3) performing iterative processing of the steps S102-S111 on the infrared image containing the noise, and ending the iterative process to obtain the final infrared image after noise reduction if the noise level difference of the infrared image after the next noise reduction is smaller than or equal to a preset threshold value T compared with the noise level of the infrared image after the previous noise reduction.
The invention has the beneficial effects that: the noise level of the infrared image is reduced, and the visual quality of the infrared image is improved.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (9)
1. A progressive infrared image noise reduction method based on noise estimation is characterized in that: the method comprises the following steps:
s101: acquiring an infrared image containing noise, and estimating the noise level of the infrared image;
s102: continuously overlapping and blocking the infrared image containing the noise with the step length of 1 to obtain image blocks;
s103: taking the central pixel of the image block as an anchor point, calculating the distance of other pixel points in the neighborhood relative to the central anchor point, and calculating a Gaussian weight matrix G1 of the neighborhood pixel by taking the distance as an independent variable;
s104: taking the central pixel of the image block as an anchor point, calculating the difference between the gray value of other pixel points in the neighborhood of the central pixel and the gray value of the central anchor point, and calculating a Gaussian weight matrix G2 of the neighborhood pixel by taking the difference of the gray values as an independent variable;
s105: performing dot multiplication on G1 and G2 to obtain a Gaussian weight matrix G3 of a joint spatial domain and a gray scale domain;
s106: performing dot multiplication on the difference of the gray values and a Gaussian weight matrix G3 to obtain weighted image blocks;
s107: performing two-dimensional Fourier transform on the weighted image blocks to obtain a frequency spectrum matrix;
s108: calculating a Gaussian weight matrix G4 by taking the frequency spectrum matrix as an independent variable;
s109: weighting and summing the frequency spectrum matrix and the Gaussian weight matrix G4, and carrying out normalization processing to obtain noise energy gradient estimation of the central anchor point pixel of the image block;
s110: subtracting the lambda times of noise energy gradient estimation from the gray value of the central anchor point pixel of the image block to obtain the gray value of the central anchor point pixel of the image block after noise reduction, and further obtain the infrared image after noise reduction;
s111: and (3) performing iterative processing of the steps S102-S111 on the infrared image containing the noise, and ending the iterative process if the noise level difference of the infrared image subjected to the next noise reduction is smaller than or equal to a preset threshold value T compared with the noise level of the infrared image subjected to the previous noise reduction, so as to obtain the final infrared image subjected to the noise reduction.
2. The progressive infrared image denoising method based on noise estimation of claim 1, wherein: step S101 specifically includes: performing convolution on an infrared image containing noise through a kernel consisting of two Laplacian masks to obtain the noise level of the infrared image;
wherein, the nucleus composed of two Laplacian masks is shown as formula (1):
n is a nucleus of the composition;
the noise level is as in equation (2):
n is the noise level; w and H represent the width and height of the noise-containing infrared image; i denotes the infrared image containing noise.
3. The progressive infrared image denoising method based on noise estimation of claim 1, wherein: in step S102, the image blocking specifically includes: symmetrically copying and expanding the infrared image containing the noise in four directions of the upper direction, the lower direction, the left direction and the right direction according to the radius and the side length of the block to obtain a symmetrically copied and expanded infrared image containing the noise, and then partitioning by taking the pixel of the infrared image containing the noise as a central point and taking the step length as 1 pixel; the calculation formula of the side length and the radius of the block is shown as the formula (3):
r＝(a-1)/2 (3)
r denotes the radius of the block and a denotes the side length of the block.
4. A method for progressive infrared image noise reduction based on noise estimation as claimed in claim 1, characterized in that: in step S103, the calculation formula of the distance between other pixel points in the neighborhood and the central anchor point is as follows (4):
where x, y represent the coordinate position of the patch, r represents the radius of the patch, and s represents the distance of the (x, y) location pixel to the anchor point.
5. The progressive infrared image denoising method based on noise estimation of claim 1, wherein: in step S103, the gaussian weight matrix G1 is represented by equation (5):
where G1 represents a Gaussian weight matrix of the neighborhood pixels with distance as argument, s represents the distance of the pixel to the anchor point, σsThe standard deviation of the gaussian function is represented.
6. The progressive infrared image denoising method based on noise estimation of claim 1, wherein: in step S104, the gaussian weight matrix G2 is expressed by equation (6):
where G2 represents a gaussian weight matrix of the neighborhood pixels with the difference in gray values as an argument, G represents the gray difference between the neighborhood pixels and the central anchor pixel, σgThe standard deviation of the gaussian function is represented.
7. The progressive infrared image denoising method based on noise estimation of claim 1, wherein: in step S108, the gaussian weight matrix G4 has the formula (7):
where F denotes the spectral matrix of the weighted block, σfThe standard deviation of the gaussian function is represented.
8. The progressive infrared image denoising method based on noise estimation of claim 1, wherein: in step S109, the noise energy gradient estimation of the image block center anchor pixel is as shown in equation (8):
gradient＝sum(|F|*G4)/sum(G4) (8)
wherein gradient represents the gradient estimation of noise energy, F represents a weighted spectrum matrix of the blocks, and | F | represents a modulus value; g4 denotes a gaussian weight matrix of the spectrum matrix of the weighted partitions, sum denotes summation.
9. The progressive infrared image denoising method based on noise estimation of claim 1, wherein: in step S110, the gray-scale value of the center anchor point pixel of the denoised image partition is as shown in formula (9):
pi＝pi-1-λ*gradient (9)
wherein p isiRepresenting the gray value, p, of the anchor pixel after the ith iteration noise reductioni-1And representing the gray value of the anchor point pixel after the i-1 th iteration noise reduction, wherein gradient represents the gradient estimation of the noise energy of the block anchor point pixel, and lambda represents the step length.
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