EdgeCS: edge guided compressive sensing reconstruction

Weihong Guo; Wotao Yin

doi:10.1117/12.863354

14 July 2010 EdgeCS: edge guided compressive sensing reconstruction

Weihong Guo, Wotao Yin

Proceedings Volume 7744, Visual Communications and Image Processing 2010; 77440L (2010) https://doi.org/10.1117/12.863354
Event: Visual Communications and Image Processing 2010, 2010, Huangshan, China

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.

Citation Download Citation

Weihong Guo and Wotao Yin "EdgeCS: edge guided compressive sensing reconstruction", Proc. SPIE 7744, Visual Communications and Image Processing 2010, 77440L (14 July 2010); https://doi.org/10.1117/12.863354

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