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Proceedings Paper

EdgeCS: edge guided compressive sensing reconstruction
Author(s): Weihong Guo; Wotao Yin
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Paper Abstract

Compressive sensing (CS) reconstructs images from a small number of projections. We propose EdgeCS - edge guided CS reconstruction - to recover images of higher qualities from fewer measurements than the current state-of-the-art methods. Accurate edge information can significantly improve image recovery quality and speed, but such information is encoded in the CS measurements of an image. To take advantage of edge information in CS recovery, EdgeCS alternatively performs CS reconstruction and edge detection in a way that each benefits from the latest solution of the other. EdgeCS is fast and returns high-quality images. It exactly recovers the 256 × 256 Shepp-Logan phantom from merely 7 radial lines (or 3.03% k-space), which is impossible for most existing algorithms. It accurately reconstructs a 512 × 512 magnetic resonance image from 21% noisy samples. Moreover, it is also able to reconstruct complex-valued images. Each took about 30 seconds on an ordinary laptop. The algorithm can be easily ported to GPUs for a speedup of more than 10 folds.

Paper Details

Date Published: 15 July 2010
PDF: 10 pages
Proc. SPIE 7744, Visual Communications and Image Processing 2010, 77440L (15 July 2010); doi: 10.1117/12.863354
Show Author Affiliations
Weihong Guo, Case Western Reserve Univ. (United States)
Wotao Yin, Rice Univ. (United States)


Published in SPIE Proceedings Vol. 7744:
Visual Communications and Image Processing 2010
Pascal Frossard; Houqiang Li; Feng Wu; Bernd Girod; Shipeng Li; Guo Wei, Editor(s)

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