Open access
Date
2012Type
- Conference Paper
ETH Bibliography
no
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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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000455173Publication status
publishedExternal links
Book title
2012 IEEE International Conference on Computational Photography (ICCP)Pages / Article No.
Publisher
IEEEEvent
Organisational unit
09695 - Studer, Christoph / Studer, Christoph
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ETH Bibliography
no
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