Rank Minimization for Snapshot Compressive Imaging

Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple frames are mapped into a single measurement, with video compressive imaging and hyperspectral compressive imaging as two representative applications. Though exciting results of high-speed videos and hyperspectral...

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Vydané v:	IEEE transactions on pattern analysis and machine intelligence Ročník 41; číslo 12; s. 2990 - 3006
Hlavní autori:	Liu, Yang, Yuan, Xin, Suo, Jinli, Brady, David J., Dai, Qionghai
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.12.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms coded aperture coded aperture compressive temporal imaging (CACTI) coded aperture snapshot spectral imaging (CASSI) Compressive sensing computational imaging Computer simulation hyperspectral images Hyperspectral imaging Image coding image processing Image reconstruction low rank Minimization nuclear norm Optimization rank minimization Self-similarity Sensors Video compression video processing Workload
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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Abstract	Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple frames are mapped into a single measurement, with video compressive imaging and hyperspectral compressive imaging as two representative applications. Though exciting results of high-speed videos and hyperspectral images have been demonstrated, the poor reconstruction quality precludes SCI from wide applications. This paper aims to boost the reconstruction quality of SCI via exploiting the high-dimensional structure in the desired signal. We build a joint model to integrate the nonlocal self-similarity of video/hyperspectral frames and the rank minimization approach with the SCI sensing process. Following this, an alternating minimization algorithm is developed to solve this non-convex problem. We further investigate the special structure of the sampling process in SCI to tackle the computational workload and memory issues in SCI reconstruction. Both simulation and real data (captured by four different SCI cameras) results demonstrate that our proposed algorithm leads to significant improvements compared with current state-of-the-art algorithms. We hope our results will encourage the researchers and engineers to pursue further in compressive imaging for real applications.
AbstractList	Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple frames are mapped into a single measurement, with video compressive imaging and hyperspectral compressive imaging as two representative applications. Though exciting results of high-speed videos and hyperspectral images have been demonstrated, the poor reconstruction quality precludes SCI from wide applications. This paper aims to boost the reconstruction quality of SCI via exploiting the high-dimensional structure in the desired signal. We build a joint model to integrate the nonlocal self-similarity of video/hyperspectral frames and the rank minimization approach with the SCI sensing process. Following this, an alternating minimization algorithm is developed to solve this non-convex problem. We further investigate the special structure of the sampling process in SCI to tackle the computational workload and memory issues in SCI reconstruction. Both simulation and real data (captured by four different SCI cameras) results demonstrate that our proposed algorithm leads to significant improvements compared with current state-of-the-art algorithms. We hope our results will encourage the researchers and engineers to pursue further in compressive imaging for real applications. Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple frames are mapped into a single measurement, with video compressive imaging and hyperspectral compressive imaging as two representative applications. Though exciting results of high-speed videos and hyperspectral images have been demonstrated, the poor reconstruction quality precludes SCI from wide applications. This paper aims to boost the reconstruction quality of SCI via exploiting the high-dimensional structure in the desired signal. We build a joint model to integrate the nonlocal self-similarity of video/hyperspectral frames and the rank minimization approach with the SCI sensing process. Following this, an alternating minimization algorithm is developed to solve this non-convex problem. We further investigate the special structure of the sampling process in SCI to tackle the computational workload and memory issues in SCI reconstruction. Both simulation and real data (captured by four different SCI cameras) results demonstrate that our proposed algorithm leads to significant improvements compared with current state-of-the-art algorithms. We hope our results will encourage the researchers and engineers to pursue further in compressive imaging for real applications.Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple frames are mapped into a single measurement, with video compressive imaging and hyperspectral compressive imaging as two representative applications. Though exciting results of high-speed videos and hyperspectral images have been demonstrated, the poor reconstruction quality precludes SCI from wide applications. This paper aims to boost the reconstruction quality of SCI via exploiting the high-dimensional structure in the desired signal. We build a joint model to integrate the nonlocal self-similarity of video/hyperspectral frames and the rank minimization approach with the SCI sensing process. Following this, an alternating minimization algorithm is developed to solve this non-convex problem. We further investigate the special structure of the sampling process in SCI to tackle the computational workload and memory issues in SCI reconstruction. Both simulation and real data (captured by four different SCI cameras) results demonstrate that our proposed algorithm leads to significant improvements compared with current state-of-the-art algorithms. We hope our results will encourage the researchers and engineers to pursue further in compressive imaging for real applications.
Author	Brady, David J. Yuan, Xin Suo, Jinli Liu, Yang Dai, Qionghai
Author_xml	– sequence: 1 givenname: Yang orcidid: 0000-0002-5787-0934 surname: Liu fullname: Liu, Yang email: y-liu16@mails.tsinghua.edu.cn organization: Department of Automation and Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing, China – sequence: 2 givenname: Xin orcidid: 0000-0002-8311-7524 surname: Yuan fullname: Yuan, Xin email: xyuan@bell-labs.com organization: Nokia Bell Labs, Murray Hill, NJ, USA – sequence: 3 givenname: Jinli orcidid: 0000-0002-3426-1634 surname: Suo fullname: Suo, Jinli email: jlsuo@tsinghua.edu.cn organization: Department of Automation and Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing, China – sequence: 4 givenname: David J. orcidid: 0000-0001-5655-2478 surname: Brady fullname: Brady, David J. email: david.brady@duke.edu organization: Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA – sequence: 5 givenname: Qionghai orcidid: 0000-0001-7043-3061 surname: Dai fullname: Dai, Qionghai email: qhdai@tsinghua.edu.cn organization: Department of Automation and Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing, China
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/30295611$$D View this record in MEDLINE/PubMed
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PublicationDate_xml	– month: 12 year: 2019 text: 2019-12-01 day: 01
PublicationDecade	2010
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PublicationTitle	IEEE transactions on pattern analysis and machine intelligence
PublicationTitleAbbrev	TPAMI
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PublicationYear	2019
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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Snippet	Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple frames are mapped into a single measurement, with video compressive...
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SubjectTerms	Algorithms coded aperture coded aperture compressive temporal imaging (CACTI) coded aperture snapshot spectral imaging (CASSI) Compressive sensing computational imaging Computer simulation hyperspectral images Hyperspectral imaging Image coding image processing Image reconstruction low rank Minimization nuclear norm Optimization rank minimization Self-similarity Sensors Video compression video processing Workload
Title	Rank Minimization for Snapshot Compressive Imaging
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