Noise reduction and edge detection via kernel anisotropic diffusion

Abstract

A novel kernel anisotropic diffusion (KAD) method is proposed for robust noise reduction and edge detection. The KAD incorporates a kernelized gradient operator in the diffusion, leading to more effective edge detection and providing a better control to the diffusion process. Adaptive diffusion threshold estimation and automatic diffusion termination criterion are also introduced to enhance the robustness of the KAD. The KAD outperforms several previous anisotropic diffusion-based methods for low SNR images.