scholarly journals Nonlocal total variation models for multiplicative noise removal using split Bregman iteration

2012 ◽  
Vol 55 (3-4) ◽  
pp. 939-954 ◽  
Author(s):  
Fangfang Dong ◽  
Haili Zhang ◽  
De-Xing Kong
Keyword(s):  
Total Variation ◽  
Multiplicative Noise ◽  
Noise Removal ◽  
Bregman Iteration ◽  
Split Bregman ◽  
Split Bregman Iteration ◽  
Nonlocal Total Variation ◽  
Multiplicative Noise Removal

scholarly journals MR Image Reconstruction Based on Iterative Split Bregman Algorithm and Nonlocal Total Variation

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Varun P. Gopi ◽  
P. Palanisamy ◽  
Khan A. Wahid ◽  
Paul Babyn
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Least Square ◽  
Split Bregman ◽  
Mr Image ◽  
Split Bregman Iteration ◽  
Nonlocal Total Variation ◽  
Comparison Results ◽  
Space Data ◽  
Mr Image Reconstruction

This paper introduces an efficient algorithm for magnetic resonance (MR) image reconstruction. The proposed method minimizes a linear combination of nonlocal total variation and least-square data-fitting term to reconstruct the MR images from undersampledk-space data. The nonlocal total variation is taken as theL1-regularization functional and solved using Split Bregman iteration. The proposed algorithm is compared with previous methods in terms of the reconstruction accuracy and computational complexity. The comparison results demonstrate the superiority of the proposed algorithm for compressed MR image reconstruction.

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.

Sign in / Sign up

Export Citation Format

Share Document