Inertial Proximal Alternating Minimization Algorithm for a Class of Nonconvex and Nonsmooth Problems

2019 ◽  
Vol 08 (07) ◽  
pp. 1228-1238
Author(s):  
梦霞 陈
Keyword(s):  
Alternating Minimization ◽  
Minimization Algorithm ◽  
Nonsmooth Problems ◽  
Alternating Minimization Algorithm

scholarly journals Line Integral Alternating Minimization Algorithm for Dual-Energy X-Ray CT Image Reconstruction

2016 ◽  
Vol 35 (2) ◽  
pp. 685-698 ◽  
Author(s):  
Yaqi Chen ◽  
Joseph A. O'Sullivan ◽  
David G. Politte ◽  
Joshua D. Evans ◽  
Dong Han ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Dual Energy ◽  
Ct Image ◽  
Alternating Minimization ◽  
Minimization Algorithm ◽  
Line Integral ◽  
X Ray ◽  
Alternating Minimization Algorithm ◽  
Ct Image Reconstruction

Alternating minimization algorithm for hybrid regularized variational image dehazing

Optik ◽  
2019 ◽  
Vol 185 ◽  
pp. 943-956 ◽  
Author(s):  
Qiaoling Shu ◽  
Chuansheng Wu ◽  
Qiuxiang Zhong ◽  
Ryan Wen Liu
Keyword(s):  
Alternating Minimization ◽  
Minimization Algorithm ◽  
Image Dehazing ◽  
Alternating Minimization Algorithm ◽  
Variational Image

Accelerated alternating minimization algorithm for Poisson noisy image recovery

2020 ◽  
Vol 28 (7) ◽  
pp. 1031-1056
Author(s):  
Anantachai Padcharoen ◽  
Duangkamon Kitkuan ◽  
Poom Kumam ◽  
Jewaidu Rilwan ◽  
Wiyada Kumam
Keyword(s):  
Image Recovery ◽  
Noisy Image ◽  
Alternating Minimization ◽  
Minimization Algorithm ◽  
Alternating Minimization Algorithm

scholarly journals A General Proximal Alternating Minimization Method with Application to Nonconvex Nonsmooth 1D Total Variation Denoising

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Xiaoya Zhang ◽  
Tao Sun ◽  
Lizhi Cheng
Keyword(s):  
Total Variation ◽  
Auxiliary Variable ◽  
Alternating Minimization ◽  
Minimization Method ◽  
Systematic Analysis ◽  
Nonsmooth Problems ◽  
Alternating Minimization Algorithm ◽  
Wide Range ◽  
Total Variation Denoising ◽  
Signal Image

We deal with a class of problems whose objective functions are compositions of nonconvex nonsmooth functions, which has a wide range of applications in signal/image processing. We introduce a new auxiliary variable, and an efficient general proximal alternating minimization algorithm is proposed. This method solves a class of nonconvex nonsmooth problems through alternating minimization. We give a brilliant systematic analysis to guarantee the convergence of the algorithm. Simulation results and the comparison with two other existing algorithms for 1D total variation denoising validate the efficiency of the proposed approach. The algorithm does contribute to the analysis and applications of a wide class of nonconvex nonsmooth problems.

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