Nonconvex Rician noise removal via convergent plug-and-play framework.

• We propose a new plug-and-play deep neural network method to remove Rician noise. • We prove some mathematical properties and the convergence of the proposed method. • Experimental results show that the proposed method outperforms existing methods. Restoring images corrupted by Rician noise is a challenging issue in medical image processing. In the existing methods, the model-driven method can not recover the images well, and the learning-based methods lack good interpretability. In this paper, we propose a plug-and-play (PnP) method to remove Rician noise. Due to the statistical properties of Rician distribution and the implicit deep image priors, the problem is non-convex. We present a convergent PnP method to address these issues by an adaptively relaxed alternating direction method of multipliers. Theoretically, we give some useful mathematical properties and the global linear convergence of the proposed method by an adaptive relaxation strategy. Experimental results show that the proposed method outperforms the existing state-of-art traditional and learning-based methods. [ABSTRACT FROM AUTHOR]