Spatial self-adaptive block-matching image denoising method based on fuzzy set theory

Abstract

The invention relates to a spatial self-adaptive block-matching image denoising method based on a fuzzy set theory, which comprises the following steps of: 1, setting the size of an initial similar block search window deltai,1; 2, calculating the mean and square-normalized symmetric distance between an image block y(Ni) of a pixel i to be treated and an image block y(Nj) of a pixel j in the search window deltai,1; 3, calculating the similarity of the image blocks according to the distance between the image blocks by utilizing fuzzy clustering analysis and performing weighted average on pixel values in the search window to obtain an estimated value of the pixel i to be treated; 4, correcting the pixel value of residual noise; and 5, increasing the size of a similar block search window deltai,n, and repeating the step 2 to the step 4 until an iterative termination condition is met. The spatial self-adaptive block-matching image denoising method based on the fuzzy set theory is reasonable in design; the effectiveness of the similarity division of the pixels is ensured; the accuracy of the estimated value is enhanced, and the performance of the block-based image denoising method is effectively improved.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a space adaptive block matching image denoising method based on fuzzy set theory. Background technique [0002] In image processing, although the local neighborhood smoothing filter can suppress the noise well and reconstruct the main structural information of the image, it cannot effectively retain the detailed information in the image, such as edge, texture and other information, because These methods assume that the original image satisfies the regularity condition, and under this assumption, details such as edges and textures are understood as noise and smoothed. In order to overcome this defect, A.Buades, B.Coll et al. proposed the Nonlocal Means (NLM) algorithm, which takes advantage of the high degree of information redundancy in natural images, that is, for a natural image For each small image block of , there are many similar image blocks in the whole image. Just like l...

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Problems solved by technology

[0005] The purpose of the present invention is to overcome the deficiencies of the prior art, to provide a space adaptive block matching image denoising method based on fuzzy set theory that can effectively improve the image denoising performance without increasing the complexity of the algorithm

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