Blind Image Denoising via Deep Unfolding Network With Degradation Information Guidance

Abstract

Blind denoising is an active research area in image processing. In recent years, denoising methods based on deep learning have achieved outstanding results. However, directly designing more complex networks is very challenging and often lacks interpretability. Besides, existing methods also often ignore the guidance of degradation information. Therefore, how to guide the design of deep neural networks by combining traditional algorithms while predicting and utilizing degradation information is an open problem. In this brief, based on the maximum a posterior (MAP) framework, we first estimate the degradation information, and design corresponding operators to obtain initial restoration estimation in high-dimensional mapping space. On this basis, the initial estimation is substituted into the denoising problem. According to the splitting algorithm and momentum based gradient descent method, the iterative optimization of the substituted problem is carried out, and the proposed algorithm framework is obtained. Furthermore, according to the proposed algorithm framework, we design the corresponding network structure, and a novel blind unfolding network named BDUNet is introduced. Experimental results show that our network not only outperforms blind methods but also has advantages over non-blind methods.