Image reconstruction by deterministic compressed sensing with chirp matrices

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationMIPPR 2009 - Medical Imaging, Parallel Processing of Images, and Optimization Techniques
DOIs
StatePublished - Dec 15 2009
EventMIPPR 2009 - Medical Imaging, Parallel Processing of Images, and Optimization Techniques: 6th International Symposium on Multispectral Image Processing and Pattern Recognition - Yichang, China
Duration: Oct 30 2009Nov 1 2009

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume7497
ISSN (Print)0277-786X

Other

OtherMIPPR 2009 - Medical Imaging, Parallel Processing of Images, and Optimization Techniques: 6th International Symposium on Multispectral Image Processing and Pattern Recognition
CountryChina
CityYichang
Period10/30/0911/1/09

Keywords

  • Chirp
  • Compressed sensing
  • Discrete chirp-Fourier transform
  • Image reconstruction

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Ni, K., Mahanti, P., Datta, S., Roudenko, S., & Cochran, D. (2009). Image reconstruction by deterministic compressed sensing with chirp matrices. In MIPPR 2009 - Medical Imaging, Parallel Processing of Images, and Optimization Techniques [74971S] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 7497). https://doi.org/10.1117/12.832649