GENERALIZED ALTERNATING PROJECTION BASED TOTAL VARIATION MINIMIZATION FOR COMPRESSIVE SENSING

被引：0
|
作者：
Yuan, Xin [1 ]
机构：
[1] Bell Labs, 600 Mt Ave, Murray Hill, NJ 07974 USA
来源：
2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2016年
关键词：
Compressive sensing; generalized alternating projection; total variation; hyperspectral imaging; video compressive sensing; coded aperture compressive temporal imaging (CACTI); code aperture snapshot spectral imaging (CASSI); ALGORITHMS; MODEL;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
We consider the total variation (TV) minimization problem used for compressive sensing and solve it using the generalized alternating projection (GAP) algorithm. Extensive results demonstrate the high performance of proposed algorithm on compressive sensing, including two dimensional images, hyperspectral images and videos. We further derive the Alternating Direction Method of Multipliers (ADMM) framework with TV minimization for video and hyperspectral image compressive sensing under the CACTI and CASSI framework, respectively. Connections between GAP and ADMM are also provided.
引用
收藏
页码：2539 / 2543
页数：5
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