Image reconstruction by deterministic compressed sensing with chirp matrices

Kangyu Ni; Prasun Mahanti; Somantika Datta; Svetlana Roudenko; Douglas Cochran

doi:10.1117/12.832649

30 October 2009 Image reconstruction by deterministic compressed sensing with chirp matrices

Kangyu Ni, Prasun Mahanti, Somantika Datta, Svetlana Roudenko, Douglas Cochran

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.

Citation Download Citation

Kangyu Ni, Prasun Mahanti, Somantika Datta, Svetlana Roudenko, and 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); https://doi.org/10.1117/12.832649

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