Sparse analysis model based multiplicative noise removal with enhanced regularization

Jing Dong, Zi-Fa Han, Yuxin Zhao, Wenwu Wang, Ales Procházka, Jonathon A. Chambers

Abstract: Highlights•This paper proposes a new multiplicative noise removal method using a sparse analysis model. Apart from a data fidelity term, two regularizers are employed in the proposed approach: a regularizer using a learned analysis dictionary and a smoothness regularizer defined on pixel-wise differences.•To address the resulting optimization problem, we adapt the alternating direction method of multipliers (ADMM) framework, and present a new optimization method.•Experimental results demonstrate the improved performance of the proposed method as compared with several recent baseline methods, especially for relatively high noise levels.