Video despeckling using Shearlet tensor-based anisotropic diffusion

Abstract

This paper provides an effective method for video speckle noise reduction based on 3D Tensor-based Anisotropic Diffusion technique in the Shearlet domain (V-STAD). The proposed model exploits the multi-scale geometric representation and the sparsity property of the shearlet transform to apply a robust tensorial diffusion at each shearlet coefficients. In fact, the robustness of diffusion tensor image smoothing was not well investigated. Therefore, we adopted the robust Tukey's biweight function in the proposed tensor-based anisotropic diffusion. By this way, the filter benefits from robust statistics and sparse directional image representation property of the shearlet transform in addition to the intrinsic temporal correlations between frames to be adopted to the anisotropic nature of diffusion tensor. The experimental results demonstrate promising despeckling solution as compared to well-known state-of-the-art video denoising methods, like VBM3D and VIDOLSAT. The proposed method has clearly shown superior despeckling capability and it simultaneously demonstrated better local image structures preservation without introducing artifacts.