Generalized alternating projection based total variation minimization for compressive sensing

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, hypersp...

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Bibliographic Details
Published inProceedings - International Conference on Image Processing pp. 2539 - 2543
Main Author Xin Yuan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2016
Subjects
Apertures
code aperture snapshot spectral imaging (CASSI)
coded aperture compressive temporal imaging (CACTI)
Compressed sensing
Compressive sensing
generalized alternating projection
Hyperspectral imaging
Minimization
total variation
video compressive sensing
Videos
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ISSN2381-8549
DOI10.1109/ICIP.2016.7532817

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Summary: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.
ISSN:2381-8549
DOI:10.1109/ICIP.2016.7532817