Image denoising method and device based on non-local mean value

Abstract

The invention provides an image denoising method based on a non-local mean value. The image denoising method comprises the step of using rotary factors and tiny scale changing factors in an image as weight factors for setting the non-local mean value. The invention further provides an image denoising device based on the non-local mean value. The image denoising device comprises a weight module which is used for using the rotary factors and the tiny scale changing factors in the image as the weight factors for setting the non-local mean value. According to the invention, the image denoising effect is improved.

Description

technical field [0001] The present invention relates to the field of image processing, in particular to an image denoising method and device based on non-local means. Background technique [0002] Image denoising is to process the input image with noise, so as to remove the noise contained in the image and restore the original noise-free image better. [0003] Buades et al. found that the entire natural image often contains structures with self-similarity, such as repeated patterns and structures. These autocorrelations contain complementary information, which is helpful for image denoising, and thus proposed a non-local mean-based denoising method. Specific steps are as follows: [0004] (1) For a certain pixel i of the input image and each pixel j within a certain search range Ω around it, select a local block N with i and j as the center pixel i and N j ; [0005] (2) By dividing the local block N i and N j Perform matching to calculate the difference between two l...

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Problems solved by technology

[0008] However, the inventors found that the existing non-local mean-based denoising methods only consider similar structures with translational characteristics when analyzing the self-similar structure in the image, so the effect of image denoising is not ideal.

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