An effective dual method for multiplicative noise removal

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• 作者：Yan Hao ; Jianlou Xu
• 关键词：Image denoising ; Multiplicative noise ; Total variation ; Dual method ; Augmented Lagrangian method ; Split Bregman ; Multi-step projection method ; Forward&ndash ; backward method
• 刊名：Journal of Visual Communication and Image Representation
• 出版年：February, 2014
• 年：2014
• 卷：25
• 期：2
• 页码：306-312
• 全文大小：1521 K

文摘

The problem of multiplicative noise removal has been widely studied recently, but most models focus on the unconstrained problems. These models require knowing the prior level of noise beforehand, however, the information is not obtained in some case and the regularization parameters are not easy to be adjusted. Thus, in the paper, we mainly study an optimization problem with total variation constraint, and propose two new denoising algorithms which compute the projection on the set of images whose total variation is bounded by a constant. In the first algorithm, we firstly give the dual formula of our model, then compute the dual problem using alternating direction method of multipliers. Experimental results show that our method is simple and efficient to filter out the multiplicative noise when the prior of noise is unknown.