Natural image denoising method based on dictionary learning and block matching

Abstract

The invention discloses a natural image denoising method based on dictionary learning and block matching, which mainly solves the problems that texture details are easily lost and homogenous areas are not smooth in the conventional natural image denoising. The method comprises the following steps of: (1) setting a denoising target function and inputting a noise-containing image z(x); (2) making an original image equal to the noise-containing image, namely y(x)=z(x), and making a dictionary D be a redundant discrete cosine transform (DCT) dictionary; (3) updating the atoms of the dictionary D and a corresponding coefficient matrix alphaij by using a kernel-singular value decomposition (KSVD) algorithm; (4) denoising the noise-containing image z(x) by using a block matching three-dimensional (BM3D) algorithm to acquire a primary denoising result; and (5) introducing the updated D and alphaij into the estimation formula of the original image to acquire the denoising result of the noise-containing image z(x). Compared with the conventional classic denoising method, the method achieves a better denoising effect and can be used for denoising a natural image; and the homogeneous area is smoothened, and the texture, the profile and the edge detail information of the image can be maintained at the same time.

Description

technical field The invention belongs to the technical field of image processing, in particular to a denoising method for sparse representation and dictionary learning, which can be used in the fields of image processing, pattern recognition, biomedicine and the like. Background technique The purpose of image denoising is to remove image noise while retaining image feature information, such as texture, edge, contour, and point-like objects. The noise of natural images has an additive background, and denoising can be carried out from both the spatial domain and the transform domain. At present, the spatial domain filtering methods with better effects include non-local mean filtering methods, image denoising under sparse representation, etc., and better transform Domain filtering methods include three-dimensional block matching method BM3D and so on. The non-local mean method determines the information compensation degree of the point to the required point by calculating the...

AI Technical Summary

Problems solved by technology

However, the DCT dictionary cannot effectively approximate the edge and detail information of the original image, and the error control method of KSVD dictionary learning is rough, which can easily cause the loss of some texture details in the original image, and this method ignores the global structure of the image

The three-dimensional block matching method BM3D is a denoising method that can effectively filte