Adaptive edge preserving denoising method based on anisotropic diffusion model

Abstract

The invention discloses a self-adaptive edge preserving denoising method based on an anisotropic diffusion model. The method comprises the following steps of preprocessing an original noise image, constructing a denoising algorithm model, performing iterative calculation on the original noise image and performing denoising processing on the original noise image. According to the method, the diffusion coefficient of the adaptive image denoising algorithm based on the combination of the fractional order differential operator and the Gaussian curvature is improved, bilateral filtering and local variance are added, the regularization item is introduced into the diffusion model, the image edge preserving effect is improved, the diffusion coefficient of the adaptive image denoising algorithm model is corrected, the denoising and edge maintaining effects are better, and the visual effect of the image is improved; the diffusion coefficient is adjusted by using the local variance so as to better control the diffusion speed; the image fidelity is improved by adding a regularization item, and an adaptive threshold is used, so that the medical image processing method is superior to a traditional image processing method in the aspect of processing a medical image besides a natural image.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an adaptive edge-preserving denoising method based on an anisotropic diffusion model. Background technique [0002] In image processing, the anisotropic diffusion model has been paid attention to. The concept of diffusion mainly comes from multi-scale description. High-resolution images can be obtained by convolving the original image with Gaussian kernel to obtain low-resolution images. This description is theoretically can be regarded as an isotropic heat conduction equation. Perona and Malik first proposed the anisotropic diffusion model (PM model). The anisotropic diffusion model can adaptively select the diffusion direction during the diffusion process. This feature ensures that the smoothness within the region takes precedence over the smoothness between regions. However, the anisotropic diffusion model has the property that the smooth region diffuses faster than ...

AI Technical Summary

Problems solved by technology

The anisotropic diffusion model can adaptively select the diffusion direction during the diffusion process. This feature ensures that the smoothness within the region takes precedence over the smoothness between regions. However, the anisotropic diffusion model has the property that the smooth region diffuses faster than the non-smooth region. feature, which causes the image to be prone to "block effect"

The fourth-order partial differential equation can overcome this challenge. This method uses the Laplacian operator in the energy function, and determines whether the image is a plane in its neighborhood by calculating whether the operator is zero. The partial differential equation will be divided The planar image of is approximated to the observation image to remove noise and preserve edges, but the disadvantage of this method is that it makes spots stand out

[0003] In practical applications, the diffusion function of the PM model cannot converge quickly for areas with obvious features, which results in smoothing of these details, and in addition, it cannot diffuse quickly in areas without obvious features

To solve this problem, researchers try to add local variance or local entropy to the diffusion coefficient to improve the protection of PM model for edge details, or combine shear wave transform (NSST) with anisotropic diffusion model. These methods Although there are certain effects, there is still room for improvement

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