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Nonlocal Denoising Method Based on Sparse Representation and Low Rank Dual Constraints

A sparse representation and non-local technology, applied in image data processing, instrumentation, computing, etc., can solve the problem of maintaining the smoothness of non-uniform regions, maintaining image edges and texture details, general image effects with high noise, and insufficient concentration of image signal energy, etc. problem, to achieve the effect of overcoming insufficient retention, improving adaptability, and easily distinguishing

Active Publication Date: 2016-05-25
XIDIAN UNIV
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Problems solved by technology

The disadvantage of this method is that the dictionary training process of this method is too complicated, and the accuracy of each atom in the dictionary cannot be guaranteed. The energy of the image signal is not concentrated enough, and it cannot maintain the smoothness of the homogeneous region while maintaining the smoothness of the image. Edge and Texture Detail
This method only has obvious denoising effect on low-noise images, and the effect on high-noise images is general.

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  • Nonlocal Denoising Method Based on Sparse Representation and Low Rank Dual Constraints
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  • Nonlocal Denoising Method Based on Sparse Representation and Low Rank Dual Constraints

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[0051] The present invention will be further described below in conjunction with the accompanying drawings.

[0052] Refer to attached figure 1 , the specific steps of the method of the present invention are as follows.

[0053] Step 1, input a noise image, the size of the image is m×n pixels.

[0054] Step 2, according to the following formula, estimate the standard deviation of the noise image:

[0055] σ=c×M{a×|vec(Y*T)-M{a×vec(Y*T)}|}

[0056] Among them, σ represents the noise standard deviation of the noise image, c represents the adjustment factor of the median filter, and the value of c is 1.4186, M{} represents the median value, a represents the adjustment factor of the low-pass filter, and the value of a is |·| indicates the operation of taking the absolute value, Y indicates the noise image matrix, T indicates a low-pass filter of a 2×2 size matrix, * indicates the convolution operation, vec indicates that the noise image matrix Y is transformed in order from le...

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Abstract

The invention discloses a sparse representation and low-rank double restraints-based nonlocal denoising method. The problem of a poor effect in an existing denoising method is mainly solved. The method comprises the following implementation steps: (1) inputting a noise image; (2) estimating a noise standard deviation; (3) setting parameters; (4) obtaining a pixel block sample set; (5) building a similar block matrix; (6) obtaining a coefficient matrix of the similar block matrix; (7) carrying out singular value filtering on the coefficient matrix; (8) obtaining the denoised coefficient matrix; (9) obtaining the denoised image matrix; (10) judging whether the maximal iterative times is achieved, if so, carrying out a step (11), or else, carrying out residuals recovery, and carrying out the step (2); and (11) outputting the denoised image matrix. The method has mating constraints on the sparsity and the constitutive property of an image signal. Compared with the prior art, the method disclosed by the invention has the advantages that structural texture information of a natural image can be kept and recovered when the noise is well smoothed.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a non-local denoising method based on sparse representation and low-rank dual constraints in the technical field of image denoising processing. The invention can be used for denoising processing of natural images, medical images and video media. Background technique [0002] Image denoising has always been a research hotspot in the field of image processing, and it is the premise of image segmentation, recognition, detection and other work. The main purpose of image denoising is to suppress the noise in the image, improve the quality of the image, and better restore the information of the image. Many noises encountered in image processing can be approximated as Gaussian white noise, so removing Gaussian white noise in images occupies an important position in many fields. [0003] Existing methods for suppressing noise, that is, filtering, are mainly divided into ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00
Inventor 张小华焦李成姚波旭王爽马文萍马晶晶钟桦吴洋林洪彬
Owner XIDIAN UNIV