30 October 2009 Image reconstruction by deterministic compressed sensing with chirp matrices
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Proceedings Volume 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques; 74971S (2009) https://doi.org/10.1117/12.832649
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
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.
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Kangyu Ni, Kangyu Ni, Prasun Mahanti, Prasun Mahanti, Somantika Datta, Somantika Datta, Svetlana Roudenko, Svetlana Roudenko, Douglas Cochran, Douglas Cochran, "Image reconstruction by deterministic compressed sensing with chirp matrices", Proc. SPIE 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 74971S (30 October 2009); doi: 10.1117/12.832649; https://doi.org/10.1117/12.832649
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