Image denoising method based on statistical local rank characteristics

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...

AI Technical Summary

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 the edge and non-edge areas of the image, which leads to the denoising results often have details and edge defects, or even lose some texture information

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