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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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2019-12, Vol.41 (12), p.2990-3006
Main Authors: Liu, Yang, Yuan, Xin, Suo, Jinli, Brady, David J., Dai, Qionghai
Format: Article
Language:English
Subjects:
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
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Summary: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.
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2018.2873587