Natural image denoising method based on regionalism and dictionary learning

Abstract

The invention discloses a natural image denoising method based on regionalism and dictionary learning. The natural image denoising method based on the regionalism and the dictionary learning mainly solves the problems that in an image denoising method based on kernel singular value decomposition (KSVD), blurring occurs in a weak texture region and fake texture occurs in a smooth region. The realization scheme includes that: removing high-frequency information of a noise-contained image through alternation of a stationary wavelet, and extracting structural information through a primal sketch algorithm, dividing the noise-contained image into three regions including a structural region, a texture region and a smooth region; obtaining a dictionary of the structural region and the texture region through a KSVD method; denoising the three regions respectively, merging denoising results, and obtaining a denoising image. An idea of combination of the regionalism and the dictionary learning is utilized, a dictionary which is obtained by the dictionary learning is enabled to conduct sparse presentation on corresponding signal composition of the image , information of edges and texture of the image is kept effectively, a denoising effect is improved, and the natural image denoising method can be used for obtaining high-quality images from noise-contained low-quality images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a natural image denoising method based on region division and dictionary learning in the technical field of image denoising, which can be used to obtain high-definition images during image denoising. Background technique [0002] Image denoising has always been an important issue in the field of image processing. Due to the limitations of imaging equipment and imaging conditions, images are inevitably polluted by noise during the process of acquisition, conversion and transmission. Therefore, image denoising has become a commonly used image preprocessing method in order to improve image quality and image recognizability. [0003] The more classic methods in the spatial domain denoising method include mean filtering, median filtering and so on. Their common feature is to use the aggregation of the pixel gray value in the local window to adjust the gray level of th...

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Problems solved by technology

[0006] The purpose of the present invention is to propose a natural image denoising method based on region division and dictionary learning to solve the problem of blurring in places with weak textures and false textures in smooth places in existing KSVD-based image denoising methods problem, improve image denoising effect

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