A New Feature-preserving Nonlinear Anisotropic Diffusion Method for Image Denoising
Zhen Qiu, Lei Yang, Weiping Lu · 2011
We present a new diffusion method for noise reduction and feature preservation.Presently, denoising methods commonly use a first-order derivative to detect edges in order to achieve a good balance between noise removal and feature preserving.However, if edges are partly lost to a certain extent or contaminated severely by noise, these methods may not be able to detect them and thus fail to preserve various features in images.To overcome this problem, we propose a new and more sophisticated feature detector by combining first-and second-order derivatives for a nonlinear anisotropic diffusion model.Numerical experiments show that the new diffusion filter outperforms many popular filters for denoising images containing edges, blobs and ridges and textures made of these features.