scholarly journals A Fast Linearized Alternating Minimization Algorithm for Constrained High-Order Total Variation Regularized Compressive Sensing

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 143081-143089 ◽  
Author(s):  
Binbin Hao ◽  
Jichao Wang ◽  
Jianguang Zhu
2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Xiao-Guang Lv ◽  
Jiang Le ◽  
Jin Huang ◽  
Liu Jun

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.


2016 ◽  
Vol 35 (2) ◽  
pp. 685-698 ◽  
Author(s):  
Yaqi Chen ◽  
Joseph A. O'Sullivan ◽  
David G. Politte ◽  
Joshua D. Evans ◽  
Dong Han ◽  
...  

Optik ◽  
2019 ◽  
Vol 185 ◽  
pp. 943-956 ◽  
Author(s):  
Qiaoling Shu ◽  
Chuansheng Wu ◽  
Qiuxiang Zhong ◽  
Ryan Wen Liu

2020 ◽  
Vol 28 (7) ◽  
pp. 1031-1056
Author(s):  
Anantachai Padcharoen ◽  
Duangkamon Kitkuan ◽  
Poom Kumam ◽  
Jewaidu Rilwan ◽  
Wiyada Kumam

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