Enhanced Compressive Imaging Using Model-Based Acquisition: Smarter sampling by incorporating domain knowledge

Aswin C. Sankaranarayanan, Matthew A. Herman, Pavan Turaga, Kevin F. Kelly

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Compressive imaging (CI) is a subset of computational photography where a scene is captured via a series of optical, transform-based modulations before being recorded at the detector. However, unlike previous transform imagers, compressive sensors take advantage of the inherent sparsity in the image and use specialized algorithms to reconstruct a high-resolution image with far lower than 100% of the total measurements. Initial CI systems exploited the properties of random matrices used in other areas of compressive sensing (CS); however, in the case of imaging, there are immense benefits to be derived by designing measurement matrices that optimize specific objectives and enable novel capabilities. In this article, we survey recent results on measurement matrix designs that provide the ability of real-time previews, signature-selective imaging, and reconstruction-free inference.

Original languageEnglish (US)
Article number7560024
Pages (from-to)81-94
Number of pages14
JournalIEEE Signal Processing Magazine
Volume33
Issue number5
DOIs
StatePublished - Sep 1 2016

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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