Weighted Schatten p-norm minimization for impulse noise removal with TV regularization and its application to medical images. (April 2021) - Wang, Li; Xiao, Di; Hou, Wen S.; Wu, Xiao Y.; Chen, Lin
Highlights: Incorporated the anisotropic total variation (TV) regularization into the WSNM-RPCA framework to improve structural smoothness for image restoration from Impulse Noise. Applied the ADMM algorithm to solve nonconvex optimization formulation. Reduced the severe punishments of small singular values. Abstract: Noise of impulse type was common in medical images. In this paper, we modeled the denoising problem for impulse noise by Weighted Schatten p -norm minimization (WSNM) with Robust Principal Component Analysis (RPCA). The anisotropic Total Variation (TV) regularization was incorporated to preserve edge information which was important for clinic detection and diagnosis. The alternating direction method of multipliers (ADMM) algorithm was adopted for solving the formulated nonconvex optimization problem. We tested the performance on both standard natural images and medical images with additive impulse noise in different levels. Experiment results implied its competitiveness compared to traditional denoising algorithms that validated to be state-of-the-art. The propose algorithm restored images with better structure information preservation outperformed the conventional techniques in terms of visual appearances. Quantitative metrics (PSNR, SSIM and FSIM) further objectively demonstrated the effectiveness of the proposed algorithm for impulse noise removal superior to the existing ones.