Adaptively weighted difference model of anisotropic and isotropic total variation for image denoising

Abstract

This paper proposes a novel nonconvex regularization functional by using an adaptively weighted difference model of anisotropic and isotropic total variation. By choosing the weights adaptively at each pixel, our model can enhance the anisotropic diffusion so as to achieve robust image recovery. Regarding to numerical implementations, we express the proposed model into a saddle point problem with the help of a dual formulation of the total variation, followed by a primal dual method to find a model solution. Numerical experiments demonstrate that the proposed approach is superior over several gradient-based methods for image denoising in terms of both visual appearance and quantitative metrics of signal noise ratio (SNR) and structural similarity index measure (SSIM).

Research and Implementation of Denoising Algorithm for Brain MRIs via Morphological Component Analysis and Adaptive Threshold Estimation

“…The noise reduction performance of deep learning The main task of image denoising is to use various filtering techniques to filter out noise while protecting important information such as image details [7][8][9]. Filtering can be carried out in time domain, such as Gaussian filter [10], Wiener filter [11], bilateral filter [12], total variation (TV) filter [13], and nonlocal mean (NLM) filter [14]; it can also be used in the transform domain, such as wavelet threshold filtering [15], and curvelet threshold filtering [16,17], etc. However, the above filtering methods are all designed to remove noise obeying Gaussian distribution.…”