An Augmented Lagrangian Method for Total Variation Video Restoration

被引:390
作者
Chan, Stanley H. [1 ]
Khoshabeh, Ramsin [1 ]
Gibson, Kristofor B. [1 ]
Gill, Philip E. [2 ]
Nguyen, Truong Q. [1 ]
机构
[1] Univ Calif San Diego, Dept Elect & Comp Engn, La Jolla, CA 92093 USA
[2] Univ Calif San Diego, Dept Math, La Jolla, CA 92093 USA
关键词
Alternating direction method (ADM); augmented Lagrangian; hot-air turbulence; total variation (TV); video deblurring; video disparity; video restoration; TOTAL VARIATION MINIMIZATION; IMAGE-CONTRAST ENHANCEMENT; LEVEL GROUPING GLG; AUTOMATIC METHOD; ALGORITHM; SUPERRESOLUTION;
D O I
10.1109/TIP.2011.2158229
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents a fast algorithm for restoring video sequences. The proposed algorithm, as opposed to existing methods, does not consider video restoration as a sequence of image restoration problems. Rather, it treats a video sequence as a space-time volume and poses a space-time total variation regularization to enhance the smoothness of the solution. The optimization problem is solved by transforming the original unconstrained minimization problem to an equivalent constrained minimization problem. An augmented Lagrangian method is used to handle the constraints, and an alternating direction method is used to iteratively find solutions to the subproblems. The proposed algorithm has a wide range of applications, including video deblurring and denoising, video disparity refinement, and hot-air turbulence effect reduction.
引用
收藏
页码:3097 / 3111
页数:15
相关论文
共 64 条
[1]   Fast Image Recovery Using Variable Splitting and Constrained Optimization [J].
Afonso, Manya V. ;
Bioucas-Dias, Jose M. ;
Figueiredo, Mario A. T. .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2010, 19 (09) :2345-2356
[2]  
[Anonymous], TR0918 NANJ U
[3]  
[Anonymous], P IEEE ICASSP
[4]  
[Anonymous], FIJI IS JUST IMAGEJ
[5]  
[Anonymous], 0931 UCLA
[6]  
[Anonymous], THESIS STANDFORD U S
[7]  
[Anonymous], 2011, DIGITAL IMAGE PROCES
[8]  
[Anonymous], Middleburry stereo dataset
[9]  
[Anonymous], 1969, Optimization
[10]  
[Anonymous], 2006, P NEURAL INFORM PROC