Multiplicative noise removing using sparse prior regulization
Guodong Wang, Zhenkuan Pan, Weizhong Zhang, Cunliang Liu, Qian Dong · 2013
Multiplicative noise removal problems have attracted much attention in recent years. In this paper, we propose a new multiplicative noise removal algorithm based on variational method. We use gradient sparse prior regulization to substitute traditional Total Variation (TV) Term. The new sparse regulization we selected is L0 smooth term. We modified the smooth term for the popular multiplicative noise removing methods. These modified methods can fit for different kind of multiplicative noise. For solving the equation, we use split method by introduce auxiliary variables. Using the sparse prior term, our method can also preserve the edges and remove the noise very well. The results show the outperforming effect of our method.