scholarly journals A New Total Variation Method for Multiplicative Noise Removal

2009 ◽  
Vol 2 (1) ◽  
pp. 20-40 ◽  
Author(s):  
Yu-Mei Huang ◽  
Michael K. Ng ◽  
You-Wei Wen
Keyword(s):  
Total Variation ◽  
Multiplicative Noise ◽  
Noise Removal ◽  
Variation Method ◽  
Multiplicative Noise Removal

scholarly journals A Fast High-Order Total Variation Minimization Method for Multiplicative Noise Removal

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Xiao-Guang Lv ◽  
Jiang Le ◽  
Jin Huang ◽  
Liu Jun
Keyword(s):  
Total Variation ◽  
Multiplicative Noise ◽  
Noise Removal ◽  
High Order ◽  
Alternating Minimization ◽  
Minimization Method ◽  
Total Variation Minimization ◽  
Alternating Minimization Algorithm ◽  
Staircase Effect ◽  
Multiplicative Noise Removal

Multiplicative noise removal problem has received considerable attention in recent years. The total variation regularization method for the solution of the noise removal problem can preserve edges well but has the sometimes undesirable staircase effect. In this paper, we propose a fast high-order total variation minimization method to restore multiplicative noisy images. The proposed method is able to preserve edges and at the same time avoid the staircase effect in the smooth regions. An alternating minimization algorithm is employed to solve the proposed high-order total variation minimization problem. We discuss the convergence of the alternating minimization algorithm. Some numerical results show that the proposed method gives restored images of higher quality than some existing multiplicative noise removal methods.

scholarly journals Multiplicative Noise Removal Based on the Linear Alternating Direction Method for a Hybrid Variational Model

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Yan Hao ◽  
Jianlou Xu ◽  
Fengyun Zhang ◽  
Xiaobo Zhang
Keyword(s):  
Total Variation ◽  
Multiplicative Noise ◽  
Splitting Method ◽  
Noise Removal ◽  
Variational Model ◽  
Total Variation Regularization ◽  
Alternating Direction ◽  
Staircase Effect ◽  
Multiplicative Noise Removal ◽  
Alternative Direction Method

To preserve the edge, multiplicative noise removal models based on the total variation regularization have been widely studied, but they suffer from the staircase effect. In this paper, to preserve the edge and reduce the staircase effect, we develop a hybrid variational model based on the variable splitting method for multiplicative noise removal; the new model is a strictly convex objective function which contains the total variation regularization and a modified regularization term. We use the linear alternative direction method to find the minimal solution and also give the convergence proof of the proposed algorithm. Experimental results verify that the proposed model can obtain the better results for removing the multiplicative noise compared with the recent method.

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