Reconstruction of compressed video via non-convex minimization

Reconstruction of compressed video via non-convex minimization

This paper studies the sparsity prior to compressed video reconstruction algorithms. An effective non-convex 3DTPV regularization (0 < p < 1) is proposed for sparsity promotion. Based on the augmented Lagrangian reconstruction algorithm, this paper analyzes and compares three non-convex proxim...

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Main Authors: Chao Ji, Jinshou Tian, Liang Sheng, Kai He, Liwei Xin, Xin Yan, Yanhua Xue, Minrui Zhang, Ping Chen, Xing Wang
Format: Article
Language:English
Published: AIP Publishing LLC 2020-11-01
Series:AIP Advances
Online Access:http://dx.doi.org/10.1063/5.0022860
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spelling doaj-b71b7a5120c948ddb77a2f1acfcdc81c2020-12-04T12:45:21ZengAIP Publishing LLCAIP Advances2158-32262020-11-011011115207115207-810.1063/5.0022860Reconstruction of compressed video via non-convex minimizationChao Ji0Jinshou Tian1Liang Sheng2Kai He3Liwei Xin4Xin Yan5Yanhua Xue6Minrui Zhang7Ping Chen8Xing Wang9Key Laboratory of Ultra-fast Photoelectric Diagnostics Technology, Xi’an Institute of Optics and Precision Mechanics (XIOPM), Chinese Academy of Sciences (CAS), Xi’an, Shaanxi 710119, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaThe Northwest Institute of Nuclear Technology, Xi’an 710024, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaKey Laboratory of Ultra-fast Photoelectric Diagnostics Technology, Xi’an Institute of Optics and Precision Mechanics (XIOPM), Chinese Academy of Sciences (CAS), Xi’an, Shaanxi 710119, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaKey Laboratory of Ultra-fast Photoelectric Diagnostics Technology, Xi’an Institute of Optics and Precision Mechanics (XIOPM), Chinese Academy of Sciences (CAS), Xi’an, Shaanxi 710119, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaThis paper studies the sparsity prior to compressed video reconstruction algorithms. An effective non-convex 3DTPV regularization (0 < p < 1) is proposed for sparsity promotion. Based on the augmented Lagrangian reconstruction algorithm, this paper analyzes and compares three non-convex proximity operators for the ℓp-norm function, and numerous simulation results confirmed that the 3DTPV regularization can gain higher video reconstruction quality than the existing convex regularization and is more competitive than the existing video reconstruction algorithms.http://dx.doi.org/10.1063/5.0022860
collection DOAJ
language English
format Article
sources DOAJ
author Chao Ji
Jinshou Tian
Liang Sheng
Kai He
Liwei Xin
Xin Yan
Yanhua Xue
Minrui Zhang
Ping Chen
Xing Wang
spellingShingle Chao Ji
Jinshou Tian
Liang Sheng
Kai He
Liwei Xin
Xin Yan
Yanhua Xue
Minrui Zhang
Ping Chen
Xing Wang
Reconstruction of compressed video via non-convex minimization
AIP Advances
author_facet Chao Ji
Jinshou Tian
Liang Sheng
Kai He
Liwei Xin
Xin Yan
Yanhua Xue
Minrui Zhang
Ping Chen
Xing Wang
author_sort Chao Ji
title Reconstruction of compressed video via non-convex minimization
title_short Reconstruction of compressed video via non-convex minimization
title_full Reconstruction of compressed video via non-convex minimization
title_fullStr Reconstruction of compressed video via non-convex minimization
title_full_unstemmed Reconstruction of compressed video via non-convex minimization
title_sort reconstruction of compressed video via non-convex minimization
publisher AIP Publishing LLC
series AIP Advances
issn 2158-3226
publishDate 2020-11-01
description This paper studies the sparsity prior to compressed video reconstruction algorithms. An effective non-convex 3DTPV regularization (0 < p < 1) is proposed for sparsity promotion. Based on the augmented Lagrangian reconstruction algorithm, this paper analyzes and compares three non-convex proximity operators for the ℓp-norm function, and numerous simulation results confirmed that the 3DTPV regularization can gain higher video reconstruction quality than the existing convex regularization and is more competitive than the existing video reconstruction algorithms.
url http://dx.doi.org/10.1063/5.0022860
work_keys_str_mv AT chaoji reconstructionofcompressedvideovianonconvexminimization
AT jinshoutian reconstructionofcompressedvideovianonconvexminimization
AT liangsheng reconstructionofcompressedvideovianonconvexminimization
AT kaihe reconstructionofcompressedvideovianonconvexminimization
AT liweixin reconstructionofcompressedvideovianonconvexminimization
AT xinyan reconstructionofcompressedvideovianonconvexminimization
AT yanhuaxue reconstructionofcompressedvideovianonconvexminimization
AT minruizhang reconstructionofcompressedvideovianonconvexminimization
AT pingchen reconstructionofcompressedvideovianonconvexminimization
AT xingwang reconstructionofcompressedvideovianonconvexminimization
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