Nonconvex Regularization with Multi-Weighted Strategy for Real Color Image Denoising

Abstract

Most existing image denoising methods commonly assume that the image is contaminated by additive white Gaussian noise (AWGN). However, real-world color image noise exhibits more complicated distribution properties, making it challenging to develop an accurate model. Consequently, denoising methods designed for AWGN often fail to achieve satisfactory performance on real-world images. In this paper, we present a novel multi-channel optimization model for real-world color images denoising within the multi-weighted Schatten $p$ -norm minimization. Our proposed model utilizes the weighted Schatten $p$ -norm as the regularization term, while the data fidelity term employs two weight matrices to balance the noise level across channels and regions. Besides, it helps to preserve as much detail as possible in the recovered image while removing noise. Although our proposed model is nonconvex and has no analytical solution, an accurate and efficient optimization algorithm is established based on the alternating direction method of multipliers (ADMMs) framework. Finally, we demonstrate the superior performance of our proposed method over existing state-of-the-art models on three real image datasets.