A new feature-preserving nonlinear anisotropic diffusion for denoising images containing blobs and ridges

Blobs and ridges underlie many important features in biological, biometric and remote sensing images. These images are likely to be corrupted by noise, such as live cells in fluorescent biological images, ridges and valleys in fingerprints and moving targets in synthetic aperture radar and infrared images. In this paper we present a diffusion method for denoising low-signal-to-ratio images containing blob and ridge features. A commonly used denoising method makes use of edge information in an image 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, such an approach may not be able to preserve these features, leading to loss of important information. To overcome this problem, we propose a novel second-order nonlocal derivative as a robust blob and ridge detector and incorporate it into a diffusion process to form a novel feature-preserving nonlinear anisotropic diffusion model. Experiments show that the new diffusion filter outperforms many popular filters for preserving blobs and ridges, reducing noise and minimizing artifacts.