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 near-periodic videos using only a low-speed camera with coded exposure and intensive off-line processing. Each low-speed...

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Bibliographic Details
Main Authors: Asif, M Salman, Reddy, D, Boufounos, P T, Veeraraghavan, A
Format: Conference Proceeding
Language:English
Subjects:
Cameras
Computational modeling
Image reconstruction
Pixel
Reconstruction algorithms
Sensors
Videos
Online Access:Request full text
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Summary: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 near-periodic 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.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2010.5652725