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dc.contributor.authorGuo, Weihong
Yin, Wotao
dc.date.accessioned 2013-07-11T14:56:46Z
dc.date.available 2013-07-11T14:56:46Z
dc.date.issued 2012-07-03
dc.identifier.citation Guo, Weihong and Yin, Wotao. "Edge Guided Reconstruction for Compressive Imaging." SIAM Journal on Imaging Sciences, 5, no. 3 (2012) Society for Industrial and Applied Mathematics: 809-834. http://dx.doi.org/10.1137/110837309.
dc.identifier.urihttps://hdl.handle.net/1911/71530
dc.description.abstract We propose EdgeCS—an edge guided compressive sensing reconstruction approach—to recover images of higher quality from fewer measurements than the current methods. Edges are important image features that are used in various ways in image recovery, analysis, and understanding. In compressive sensing, the sparsity of image edges has been successfully utilized to recover images. However, edge detectors have not been used on compressive sensing measurements to improve the edge recovery and subsequently the image recovery. This motivates us to propose EdgeCS, which alternatively performs edge detection and image reconstruction in a mutually beneficial way. The edge detector of EdgeCS is designed to faithfully return partial edges from intermediate image reconstructions even though these reconstructions may still have noise and artifacts. For complex-valued images, it incorporates joint sparsity between the real and imaginary components. EdgeCS has been implemented with both isotropic and anisotropic discretizations of total variation and tested on incomplete k-space (spectral Fourier) samples. It applies to other types of measurements as well. Experimental results on large-scale real/complex-valued phantom and magnetic resonance (MR) images show that EdgeCS is fast and returns high-quality images. For example, it exactly recovers the 256×256 Shepp–Logan phantom from merely 7 radial lines (3.03% k-space), which is impossible for most existing algorithms. It is able to accurately reconstruct a 512 × 512 MR image with 0.05 white noise from 20.87% radial samples. On complex-valued MR images, it obtains recoveries with faithful phases, which are important in many medical applications. Each of these tests took around 30 seconds on a standard PC. Finally, the algorithm is GPU friendly.
dc.language.iso eng
dc.publisher Society for Industrial and Applied Mathematics
dc.title Edge Guided Reconstruction for Compressive Imaging
dc.type Journal article
dc.contributor.funder National Science Foundation
dc.contributor.funder Office of Naval Research
dc.contributor.funder Alfred P. Sloan Foundation
dc.citation.journalTitle SIAM Journal on Imaging Sciences
dc.subject.keywordcompressive sensing
edge detection
total variation
discrete Fourier transform
magnetic resonance imaging
dc.citation.volumeNumber 5
dc.citation.issueNumber 3
dc.embargo.terms none
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1137/110837309
dc.identifier.grantID CAREER award DMS-07-48839 (National Science Foundation)
dc.identifier.grantID N00014- 08-1-1101 (Office of Naval Research)
dc.identifier.grantID Research Fellowship (Alfred P. Sloan Foundation)
dc.citation.firstpage 809
dc.citation.lastpage 834


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