@inproceedings{955c2e7cbca54c34a9111c4c361b3896,
title = "Image reconstruction by deterministic compressed sensing with chirp matrices",
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.",
keywords = "Chirp, Compressed sensing, Discrete chirp-Fourier transform, Image reconstruction",
author = "Kangyu Ni and Prasun Mahanti and Somantika Datta and Svetlana Roudenko and Douglas Cochran",
year = "2009",
doi = "10.1117/12.832649",
language = "English (US)",
isbn = "9780819478085",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
booktitle = "MIPPR 2009 - Medical Imaging, Parallel Processing of Images, and Optimization Techniques",
note = "MIPPR 2009 - Medical Imaging, Parallel Processing of Images, and Optimization Techniques: 6th International Symposium on Multispectral Image Processing and Pattern Recognition ; Conference date: 30-10-2009 Through 01-11-2009",
}