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De-noising method integrated with gradient histogram and low rank constraint

A gradient histogram, low-rank constraint technology, applied in image enhancement, image data processing, instruments, etc., can solve the problem of poor denoising effect of denoising methods, and achieve the effect of enhancing denoising performance

Active Publication Date: 2017-11-17
GUILIN UNIV OF ELECTRONIC TECH
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Problems solved by technology

[0003] The technical problem to be solved by the present invention is that the existing denoising method has poor denoising effect, and a denoising method combining gradient histogram and low rank constraint is provided

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  • De-noising method integrated with gradient histogram and low rank constraint
  • De-noising method integrated with gradient histogram and low rank constraint
  • De-noising method integrated with gradient histogram and low rank constraint

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Embodiment Construction

[0043] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings in specific embodiments of the present invention.

[0044] A denoising method that combines gradient histograms with low-rank constraints, such as figure 1 As shown, it specifically includes the following steps:

[0045] Step 1, read the noisy image, and obtain the matrix expression y of the image;

[0046] Step 2, logarithmically transform the noisy image to obtain the noisy image f in the logarithmic domain, and initialize the number of iterations k=1, z (0) =f, where z (0) is the image to be restored in the logarithmic domain, is the gradient of z;

[0047] Step 3, use the sliding window technique to convert the noisy image z (k-1) Divide into small blocks of 7×7, and use K-means to cluster these small image blocks into 70 categories;

[0048] Step 4, use the principal component analysis method to calculate the sub-dictionary in ea...

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Abstract

The invention discloses a de-noising method integrated with a gradient histogram and low rank constraint. Based on sparse priori and other non-local self similar priori, sparse expression advantages are utilized, and a non-local regular item, a gradient regular item and a low rank constraint item are added to remove multiplicative noise. The method is advantaged in that a multiplicative noise model is converted through logarithmic transformation into an additive noise model of a logarithm domain, a training dictionary of the noise image in the logarithm domain is utilized, image gradient histogram estimation and low rank constraint are combined, local and non-local image connection is enhanced, on the condition that de-noising is effectively realized, the image texture information is better reserved; the experiment result is relatively good in subjective vision and objective evaluation indexes, the precise texture structure of the image is reserved to a great degree, and the image after de-noising is more intelligible.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, and in particular relates to a denoising method combining gradient histograms and low rank constraints. Background technique [0002] Image noise removal is one of the most basic problems in digital image processing. Images are more or less inevitably polluted by random noise. The mathematical model of multiplicative noise is: y=x v, where y is a noisy image, and x represents Original image, v is noise. Sparse priors and non-local self-similar priors are widely used in image denoising. In addition, the joint use of sparse priors and other non-local self-similar priors has also resulted in many advanced image denoising algorithms. Such as using curvelet transform as its structure and using l 1 Image denoising algorithm with norm acting on sparsely coded coefficients (DFN model), image denoising algorithm by sparse regularization variation with MAP estimation and sparse represen...

Claims

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T5/70
Inventor 陈利霞李佳宇王学文何成凤
Owner GUILIN UNIV OF ELECTRONIC TECH
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