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Streaming Compressive Sensing for high-speed periodic videos

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Streaming Compressive Sensing for high-speed periodic videos
The ability of Compressive Sensing (CS) to recover sparse signals from limited measurements has been recently exploited in computational imaging to acquire high-speed periodic and nearperiodic videos using only a low-speed camera with coded exposure and intensive off-line processing. Each low-speed frame integrates a coded sequence of high-speed frames during its exposure time. The high-speed video can be reconstructed from the low-speed coded frames using a sparse recovery algorithm. This paper presents a new streaming CS algorithm specifically tailored to this application. Our streaming approach allows causal on-line acquisition and reconstruction of the video, with a small, controllable, and guaranteed buffer delay and low computational cost. The algorithm adapts to changes in the signal structure and, thus, outperforms the off-line algorithm in realistic signals. ICIP 2010 This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy ...
M. Salman Asif, Dikpal Reddy, Petros Boufounos, As
Added 12 Feb 2011
Updated 12 Feb 2011
Type Journal
Year 2010
Where ICIP
Authors M. Salman Asif, Dikpal Reddy, Petros Boufounos, Ashok Veeraraghavan
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