Dynamical Threshold-Based Fractional Anisotropic Diffusion for Speckle Noise Removal
Jiali Wei, Xiaofeng Liao
Abstract
The study of effective methods for removing image speckle remains a significant challenge in image processing. In contrast to additive noise, speckle noise is a multiplicative noise whose intensity is proportional to the signal. This results in a noise distribution that exhibits a high dependence on the signal intensity throughout the image, rendering it difficult to remove. Therefore, we present a novel approach to speckle noise removal using dynamical threshold-based fractional anisotropic diffusion (named as DTFAD) in this study. The method simultaneously considers both gradient and gray scale information in the image. In addition, the fractional derivative is integrated with anisotropic diffusion in the DTFAD model, which enhances the image denoising effect to preserve the fundamental features and edges of the image. The design of a dynamic threshold function in the diffusion coefficient enables the diffusion pattern and intensity to adaptively change according to image information, thus effectively removing speckle noise. We establish the well-posedness of the DTFAD model and implement it using an explicit finite difference scheme. Extensive experiments demonstrate that the DTFAD model outperforms traditional anisotropic diffusion techniques, and achieves a superior balance between denoising performance and texture preservation. This evidence demonstrates that the DTFAD model has the potential to be applied in practical engineering.