An image denoising algorithm based on gamma norm minimization

A gamma norm, minimization technology, applied in image enhancement, image data processing, computing and other directions, can solve the problem of denoising and only obtain sub-optimal solutions.

Active Publication Date: 2019-04-23
DALIAN UNIVERSITY
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Problems solved by technology

However, using the kernel norm to approximate the matrix rank function overly penalizes large singular values, resulting in suboptimal solutions to the denoising problem

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  • An image denoising algorithm based on gamma norm minimization
  • An image denoising algorithm based on gamma norm minimization
  • An image denoising algorithm based on gamma norm minimization

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Embodiment Construction

[0077] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0078] The concrete steps that realize the image denoising algorithm based on gamma norm minimization of the present invention are:

[0079] 1. Establish a low-rank denoising model

[0080] The principle of the low-rank denoising method can be described as follows: the overlapping noise image y of size M×N is divided into n size image block y i ,i=1,2,...,n. Then search for the current image block y in a window of size L×L i The most similar m image patches, and construct them as a similar image patch matrix Y in the form of a column vector i ∈ R d×m , namely Y i =(y i,1 ,y i,2 ,...,y i,m ), y i,m Indicates the current image block y i The mth similar image block of . Based on this, the low-rank denoising problem can be expressed as the following optimization problem:

[0081]

[0082] Among them, Y i is the matrix of noise sim...

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Abstract

The invention belongs to the field of digital image processing, and relates to an image denoising algorithm based on gamma norm minimization. The algorithm comprises the following steps of overlappingand partitioning noise images; adaptively searching a plurality of non-local image blocks most similar to the current image block based on the structural similarity index to form a similar image block matrix; and then utilizing a non-convex gamma norm unbiased approximation matrix rank function to construct a low-rank denoising model, finally, solving the obtained low-rank denoising optimizationproblem based on singular value decomposition, and recombining the denoised image blocks into a denoised image. Simulation results show that compared with existing PID, NLM, BM3D and NNM algorithms, the algorithm provided by the invention can effectively eliminate the Gaussian noise, and can better recover the original image details.

Description

technical field [0001] The invention belongs to the field of digital image processing, in particular to an image denoising algorithm based on gamma norm minimization. Background technique [0002] Digital images are inevitably polluted by noise in the process of acquisition and transmission, resulting in the loss of image details and quality degradation, which in turn affects subsequent image processing. The purpose of image denoising is to restore the original image x as accurately as possible from the noisy image y, and preserve important details such as edges and textures. The degradation model of the denoising problem can be expressed as: y=x+v, where v is usually assumed to have a mean of 0 and a variance of σ n 2 Gaussian white noise. Due to the ill-posed nature of the image denoising problem, it is particularly important to use the prior knowledge representing the statistical characteristics of the image for denoising. [0003] In recent years, many image denoisin...

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T5/70Y02T10/40
Inventor 王洪雁王拓张莉彬
Owner DALIAN UNIVERSITY
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