Image denoising method based on statistical local rank characteristics

A statistical local and local rank technology, applied in the field of image processing, can solve the problems of losing texture information, details and edge defects, not considering image edge and non-edge areas, etc., to achieve the best denoising effect, guarantee effectiveness and reliability The effect of preserving image edge detail information

Active Publication Date: 2016-08-24
上海厉鲨科技有限公司
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Problems solved by technology

However, the above image denoising methods based on sparse representation are all processing the entire image, without considering the difference between

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  • Image denoising method based on statistical local rank characteristics
  • Image denoising method based on statistical local rank characteristics
  • Image denoising method based on statistical local rank characteristics

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[0023] The specific implementation manner and working principle of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0024] Such as figure 1 As shown, an image denoising method based on statistical local rank features is carried out in the following steps:

[0025] First enter step 1: For image I, use the local rank operator according to the formula LRT k (I)={LRT k (I i )|I i ∈I} performs local rank transformation under different parameter conditions to obtain the positive local rank transformation LRT of the image pk (I i ) and negative local rank transform LRT nk (I i );

[0026] In specific implementation, the following local rank transformation operator can be used to perform local rank transformation on the image:

[0027] LRT δ (I)={LRT δ (I i )|I i ∈I},

[0028] in,

[0029] LRT δ ( I i ...

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Abstract

The invention discloses an image denoising method based on statistical local rank characteristics. The image denoising method comprises the following steps: performing local rank transformation on the image under different parameter conditions by utilizing a local rank operator, thereby obtaining positive local rank transformation and negative local rank transformation of the image; adding the local rank transformation and negative local rank transformation to obtain statistical local rank characteristics with continuous parameter changes; taking the statistical local rank characteristics as constraint conditions on the basis of an image denoising method with sparse representation, and performing primary denoising on the image; and finally, performing secondary denoising on the image by controlling the difference of the statistical local rank characteristics between the images before and after denoising, and removing the image noise, thereby obtaining a final clear image. The method has the obvious effects that compared with the traditional denoising method based on sparse representation, the method has a better denoising effect, can acquire a denoised image with high quality, and further can effectively guarantee the reliability of subsequent image processing and analyzing.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image denoising method based on statistical local rank features. Background technique [0002] In the process of image acquisition and transmission, it will inevitably be affected by various noises, resulting in the degradation of image quality, which cannot meet the needs of subsequent processing. In order to improve image quality, image denoising technology came into being. [0003] In recent years, sparse representations of signals have emerged as a powerful tool for high-dimensional signal acquisition, characterization, and compression. The sparse representation model assumes that the non-noise components in the image can be sparsely represented, but the noise components cannot be sparsely represented. Researchers have carried out a lot of research using this characteristic of the sparse representation method. Based on the K-singular value decomposition algorith...

Claims

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T5/002
Inventor 李正浩陈魏然杨隽莹陈凯龚卫国李伟红杨利平胡伦庭
Owner 上海厉鲨科技有限公司
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