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Proceedings Paper

Image reconstruction by deterministic compressed sensing with chirp matrices
Author(s): Kangyu Ni; Prasun Mahanti; Somantika Datta; Svetlana Roudenko; Douglas Cochran
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Paper Abstract

A recently proposed approach for compressed sensing, or compressive sampling, with deterministic measurement matrices made of chirps is applied to images that possess varying degrees of sparsity in their wavelet representations. The "fast reconstruction" algorithm enabled by this deterministic sampling scheme as developed by Applebaum et al. [1] produces accurate results, but its speed is hampered when the degree of sparsity is not sufficiently high. This paper proposes an efficient reconstruction algorithm that utilizes discrete chirp-Fourier transform (DCFT) and updated linear least squares solutions and is suitable for medical images, which have good sparsity properties. Several experiments show the proposed algorithm is effective in both reconstruction fidelity and speed.

Paper Details

Date Published: 30 October 2009
PDF: 8 pages
Proc. SPIE 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 74971S (30 October 2009); doi: 10.1117/12.832649
Show Author Affiliations
Kangyu Ni, Arizona State Univ. (United States)
Prasun Mahanti, Arizona State Univ. (United States)
Somantika Datta, Arizona State Univ. (United States)
Svetlana Roudenko, Arizona State Univ. (United States)
Douglas Cochran, Arizona State Univ. (United States)

Published in SPIE Proceedings Vol. 7497:
MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques
Faxiong Zhang; Faxiong Zhang, Editor(s)

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