Deep side group sparse coding network for image denoising

Abstract

Recently, deep learning has made significant progress in image denoising. However, most of existing deep learning based methods are purely data-driven, without considering the knowledge of image denoising. Moreover, the parameters of deep denoising network are not explainable. According to these issues, this paper proposes a deep side group sparse coding network for image denoising, named a side group sparse coding (SGSC)-Net. First, SGSC model for image denoising by exploiting prior information regarding the group sparse coefficients consistency is developed. Specifically, the side information is constructed as the weighted combination of intermediate estimations, and updated iteratively. Then, the optimisation solution of SGSC model is turned into a deep neural network using deep unfolding, that is, SGSC-Net. The computational path of SGSC-Net fully follows the iterations of optimisation solution, and consequently the network parameters are interpretable. Furthermore, the design of SGSC-Net employs the insight of SGSC denoising model. The experimental results on well-known datasets quantitatively and qualitatively demonstrate that SGSC-Net is competitive to existing deep unfolding-based and typical deep neural network-based methods.