A New Feature-preserving Nonlinear Anisotropic Diffusion Method for Image Denoising

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.