Total variation regularized multi-matrices weighted Schatten p-norm minimization for image denoising

Motivated by the superior performance of nonconvex nonsmooth L-p (0 < p < 1) norm, this paper introduces a novel method that combines the weighted Schatten p-norm, L-p-norm, and total variation regularization based on the multiple matrices denoising framework. The weighted Schatten p-norm encodes the global low-rank to multiple matrices data, while the L-p-norm provides noise robustness. Total variation regularization is incorporated to promote structural smoothness and edge preservation in the image. To solve the nonconvex and nonsmooth model, an efficient alternating direction method of multipliers (ADMM) is designed. In addition, we discuss how the value of p in the three items affects the denoising performance in simulated images. Extensive experiments on face datasets, videos, and real-world noisy images demonstrate that the proposed method significantly improves denoising performance, particularly for removing large sparse noise.

Automated summary

Motivated by the superior performance of nonconvex nonsmooth L-p (0 < p < 1) norm, this paper introduces a novel method that combines the weighted Schatten p-norm, L-p-norm, and total variation regularization based on the multiple matrices denoising framework. An efficient alternating direction method of multipliers (ADMM) is designed to solve the nonconvex and nonsmooth model. Extensive experiments on face datasets, videos, and real-world noisy images demonstrate that the proposed method significantly improves denoising performance, particularly for removing large sparse noise.