Memory-based Speckle Reducing Anisotropic Diffusion

Abstract

Diffusion filters are usually modelled as partial differential equations (PDEs) and used to reduce image noise without affecting the image main features. However, they have a drawback of broadening object boundaries and dislocating edges. Such drawbacks limit the ability of diffusion techniques applied to image processing. Yu and Acton. introduced the speckle reducing anisotropic diffusion (SRAD) to reduce speckle noise from ultrasound (US) and synthetic aperture radar (SAR) images. Incorporating the instantaneous coefficient of variation (ICOV) as the diffusion coefficient and edge detector, SRAD gives significantly enhanced images where most of the speckle noise is reduced. Yet, SRAD still faces the same problem of ordinary diffusion filters where the boundary broadening and edge dislocation affect its overall performance. In this paper, we introduce a novel approach to the diffusion filtering process, where a memory term is introduced as a reaction-diffusion term. By applying our new memory-based diffusion to SRAD, we significantly reduced the boundary broadening and edge dislocation effect and enhanced the diffusion process itself. Experimental results showed that the performance of our proposed memory-based scheme surpass other diffusion filters like normal SRAD and Perona-Malik filter as well as various adaptive linear de-noising filters.