Anisotropism filtering method based on self-adaptive averaging factor

Abstract

The invention relates to the technical field of digital image processing, and aims at avoiding a staircase effect and a block effect by conducting improvement on a traditional anisotropism filtering method. According to an anisotropism filtering method based on a self-adaptive averaging factor, in the image filtering process, edge fragmentary information is protected by reducing the smoothing degree of noise and marginal areas. Therefore, according to the technical scheme, the anisotropism filtering method based on the self-adaptive averaging factor comprises the steps that pretreatment is conducted on a noise image by adopting a Gaussian filter, the pretreatment formula comprises the step that an improved anisotropic filtering is utilized, the size of the value of a parameter K is determined according to differences of gradient values of each diffusion pixel to a central pixel, that is to say, a self-adaptive equation is utilized to replace the value of an original fixed parameter K, the value of the K of the improved anisotropic filtering is made to reduce on the noise and marginal areas, and the smoothing degree of the improved anisotropic filtering is reduced; the value of the K of the improved anisotropic filtering is increased on the smooth and flat areas, and the smoothing degree of the improved anisotropic filtering is increased. The anisotropism filtering method based on the self-adaptive averaging factor is mainly applied to digital image processing.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to an anisotropic filter based on image edge protection, which is used for eliminating Gaussian noise in an image while protecting image details. technical background [0002] The anisotropic denoising method was first proposed in the 1990s, and it is a relatively popular image denoising research method in recent years. The anisotropic denoising method takes the central pixel as the benchmark, and diffuses to the four directions of up, down, left and right respectively, such as figure 1 shown. In this method, the gradient threshold is used to distinguish whether the change of image gray value is caused by noise or edge, and then the nonlinear diffusion equation whose initial value is the original image is solved. The traditional anisotropic filtering formula is as follows: [0003] ∂ ...

AI Technical Summary

Problems solved by technology

However, in the traditional anisotropic filtering method, the parameter K is constant in each iteration, no matter how much the gradient value of the diffusion pixel is, this method diffuses with the same smoothness, so for high-intensity noise, use this The result of algorithm diffusion may enhance the noise instead, thus producing serious step effect or block effect

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