CS-MUVI: Video compressive sensing for spatial-multiplexing cameras

Aswin C. Sankaranarayanan, Christoph Studer, Richard G. Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

96 Scopus citations

Abstract

Compressive sensing (CS)-based spatial-multiplexing cameras (SMCs) sample a scene through a series of coded projections using a spatial light modulator and a few optical sensor elements. SMC architectures are particularly useful when imaging at wavelengths for which full-frame sensors are too cumbersome or expensive. While existing recovery algorithms for SMCs perform well for static images, they typically fail for time-varying scenes (videos). In this paper, we propose a novel CS multi-scale video (CS-MUVI) sensing and recovery framework for SMCs. Our framework features a co-designed video CS sensing matrix and recovery algorithm that provide an efficiently computable low-resolution video preview. We estimate the scene's optical flow from the video preview and feed it into a convex-optimization algorithm to recover the high-resolution video. We demonstrate the performance and capabilities of the CS-MUVI framework for different scenes.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Computational Photography, ICCP 2012
DOIs
StatePublished - Jul 16 2012
Event2012 IEEE International Conference on Computational Photography, ICCP 2012 - Seattle, WA, United States
Duration: Apr 28 2012Apr 29 2012

Other

Other2012 IEEE International Conference on Computational Photography, ICCP 2012
CountryUnited States
CitySeattle, WA
Period4/28/124/29/12

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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