NDLI: Using reed-muller sequences as deterministic compressed sensing matrices for image reconstruction

Content Provider	IEEE Xplore Digital Library
Author	Kangyu Ni Datta, S. Mahanti, P. Roudenko, S. Cochran, D.
Copyright Year	2010
Description	Author affiliation: School of Mathematical and Statistical Sciences, Arizona State University, USA (Kangyu Ni; Roudenko, S.; Cochran, D.) \|\| School of Electrical, Computer, and Energy Engineering, Arizona State University, USA (Mahanti, P.) \|\| Program in Applied and Computational Mathematics, Princeton University, USA (Datta, S.)
Abstract	An image reconstruction algorithm using compressed sensing (CS) with deterministic matrices of second-order Reed-Muller (RM) sequences is introduced. The 1D algorithm of Howard et al. using CS with RM sequences suffers significant loss in speed and accuracy when the degree of sparsity is not high, making it inviable for 2D signals. This paper describes an efficient 2D CS algorithm using RM sequences, provides medical image reconstruction examples, and compares it with the original 2DCS using noiselets. This algorithm entails several innovations that enhance its suitability for images: initial best approximation, a greedy algorithm for the nonzero locations, and a new approach in the least-squares step. These enhancements improve fidelity, execution time, and stability in the context of image reconstruction.
Starting Page	465
Ending Page	468
File Size	262300
Page Count	4
File Format	PDF
ISBN	9781424442959
ISSN	15206149
DOI	10.1109/ICASSP.2010.5495714
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Compressed sensing Image reconstruction Symmetric matrices Biomedical imaging Pixel Computational complexity Mathematics Power engineering and energy Approximation algorithms Technological innovation Image Reconstruction Compressed Sensing Reed-Muller Sequences
Content Type	Text
Resource Type	Article

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