Rank Minimization for Snapshot Compressive Imaging

Yang Liu, Xin Yuan, Jinli Suo, David J. Brady, Qionghai Dai

Research output: Contribution to journalArticlepeer-review

75 Scopus citations

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.

Original languageEnglish (US)
Article number8481592
Pages (from-to)2990-3006
Number of pages17
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume41
Issue number12
DOIs
StatePublished - Dec 1 2019
Externally publishedYes

Keywords

  • Compressive sensing
  • coded aperture
  • coded aperture compressive temporal imaging (CACTI)
  • coded aperture snapshot spectral imaging (CASSI)
  • computational imaging
  • hyperspectral images
  • image processing
  • low rank
  • nuclear norm
  • rank minimization
  • video processing

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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