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

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 cumber...

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Bibliographic Details
Published in2012 IEEE International Conference on Computational Photography pp. 1 - 10
Main Authors Sankaranarayanan, A. C., Studer, C., Baraniuk, R. G.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2012
Subjects
Cameras
Computer architecture
Decision support systems
Optical sensors
Spatial resolution
Vectors
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ISBN1467316601
9781467316606
DOI10.1109/ICCPhot.2012.6215212

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Summary: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.
ISBN:1467316601
9781467316606
DOI:10.1109/ICCPhot.2012.6215212