Rician Noise Removal in Low-noise Condition via a Deep Unfolding Network

Abstract

Nuclear magnetic resonance (NMR) signals as the key role of research on denoising, are mainly influenced by Rician noise. Restoring clean Rician medical images from signal-related Rician noise is a challenging task with great practical significance. In this paper, an energy function based on MAP estimation is proposed, mainly for conditions of low noise, where the noise distribution is approximately Gaussian. Then the mathematical model is embedded into the network structure to realize the knot of knowledge-driven and network learning. The experiment shows that the proposed model only uses simple and lightweight network modules and a small amount of training data, which has a better denoising effect and has achieved satisfactory results under the two evaluation indexes.