Image denoising method and system based on threshold value block matching

Abstract

The invention discloses an image denoising method and system based on threshold value block matching. The image denoising method includes acquiring several images to be processed in time succession, calculating threshold values matched with blocks according to brightness of the images to be processed and processing by fuzzy filter to obtain fuzzy images, layering according to calculation distance of SAD (sum of absolute difference), and matching the blocks of the fuzzy images according to layering search; using the first image of the continuous images to be processed as a reference image, accumulatively processing the images to be processed one by one, and accumulating the image blocks with SAD smaller than the threshold values to the corresponding positions of the reference image so as to obtain cumulative sum and the images; dividing color values of various pixel points in the cumulative sum and the images from the cumulative frequency of the pixel points correspondingly to obtain denoised images. Accordingly, block matching efficiency is greatly improved, accuracy in matching is ensured, denoising effect is improved, and the image denoising method can be conveniently introduced into the industrial production.

Description

technical field [0001] The invention relates to the field of image technology, in particular to an image denoising method based on threshold block matching and a system applying the method. Background technique [0002] At present, our method of removing noise from images is to use a single photo for processing, such as a local neighborhood smoothing filter. Although it can suppress noise well and reconstruct the main structural information of the image, it cannot effectively retain Details in the image, such as edges and textures, are because these methods assume that the original image satisfies regularity conditions. Under this assumption, details such as edges and textures are understood as noise and smoothed. [0003] 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, there are...

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Problems solved by technology

However, the calculation of the full search is too large, and the speed is surprisingly slow, and the block with the smallest SAD is not the optimal block-it may happen that in a smooth area, the block with a larger cumulative SAD will have a lower denoising effect. Blocks smaller than cumulative SAD are more prominent

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