Flexible parameter selection methods for Rician noise removal with convergence guarantee

Restoring images corrupted by Rician noise is a challenging issue in the field of medical image processing. In the existing variational methods, there is a balancing parameter between the regularization term and the fidelity term. However, it is very hard to find the optimal parameter. In this paper, we study the total variation-based Rician noise removal model with spatially varying parameters. We propose flexible and automatic parameter selection strategies to balance the regularization extent between different kinds of image regions. A modified alternating direction method of multipliers is derived to solve the non-convex model efficiently. Theoretically, we prove that if a selection strategy satisfies some reasonable conditions, the convergence of the proposed algorithm is guaranteed. Numerical results demonstrate that the proposed method with automatic parameter selection can better preserve the structures and fine textures than other closely related methods.