Image denoising method through combination of adaptive nonlocal samples and low rank

A non-local and adaptive technology, applied in image enhancement, image analysis, image data processing and other directions, can solve the problems of destroying the low rank of the image, maintaining the edges and contours and other problems, achieving rich content, overcoming the lack of adaptability, The effect of maintaining details and textures

Active Publication Date: 2017-10-24
GUILIN UNIV OF ELECTRONIC TECH
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Problems solved by technology

Although the above method has achieved good results in image denoising, it has certain shortcomings in maintaining edges and contours.
[0004] Due to the certain redundant information and sim...

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  • Image denoising method through combination of adaptive nonlocal samples and low rank
  • Image denoising method through combination of adaptive nonlocal samples and low rank
  • Image denoising method through combination of adaptive nonlocal samples and low rank

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Embodiment Construction

[0035] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific examples and accompanying drawings.

[0036] refer to figure 1 , an image denoising method combining adaptive non-local samples and low rank, the specific implementation steps are as follows:

[0037] Step 1. Read the noisy image y in the real number domain.

[0038] Step 2: Perform logarithmic transformation on the noisy map y in the real number domain to obtain the noisy map Y in the logarithmic domain.

[0039] Step 3. Use the block allocation technology to divide the noisy image Y in the logarithmic domain into 7×7 small blocks, and use the non-local similarity matching algorithm to find the similar blocks of each image block, and then calculate the similarity through the Euclidean distance. The most similar m image patches form an image group in is the y of the noisy im...

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Abstract

The invention discloses an image denoising method through combination of adaptive nonlocal samples and low rank. Firstly an image is transformed to a logarithmic domain by using logarithmic transformation, and a multiplicative noise model is transformed into an additive noise model; the image is partitioned and grouping is performed according to the similarity so as to obtain image groups having similar blocks; then low-tank approximation processing is performed on the image groups so as to obtain an initial estimation value; then adaptive nonlocal sample model processing is performed on the initial estimation value so as to obtain a logarithmic domain recovery result; and finally the logarithmic domain image is restored to a real number domain by using exponential transformation and corrected so as to obtain a final denoised image. The experiment result indicates that the method has great robustness for the multiplicative noise, and the great peak signal-to-noise ratio and the structural similarity can be obtained and the visual quality of the image can be greatly improved for the image having the multiplicative noise.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to an image denoising method combining adaptive non-local samples and low rank. Background technique [0002] The development history of image denoising technology began in the 1950s. Due to the technical defects and external interference in image acquisition and transmission, it is necessary to seek new technologies to make up for these shortcomings and improve image quality. With the rapid development of science and technology, image processing technology has received extensive attention and achieved pioneering achievements in the application fields of people's life, astronomy, biomedicine, VR technology, artificial intelligence, public security, justice, culture and art. The quality of the image will be directly related to the subsequent processing of the image. In the past ten years, many researchers have been looking for various ideas to process images, and ho...

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Application Information

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IPC IPC(8): G06T5/00G06K9/62
CPCG06T5/002G06T2207/20021G06T2207/20012G06V10/751
Inventor 陈利霞刘俊丽王学文李其珂
Owner GUILIN UNIV OF ELECTRONIC TECH
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