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 ...

• Type: Journal
• Year: 2010
• Where: ICIP
• Authors: M. Salman Asif, Dikpal Reddy, Petros Boufounos, Ashok Veeraraghavan
• Keywords: Algorithm, Electric Research Laboratories, ICIP 2010, Image Processing, Mitsubishi Electric Research