Edge Guided Reconstruction for Compressive Imaging

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dc.citation.firstpage809en_US
dc.citation.issueNumber3en_US
dc.citation.journalTitleSIAM Journal on Imaging Sciencesen_US
dc.citation.lastpage834en_US
dc.citation.volumeNumber5en_US
dc.contributor.authorGuo, Weihongen_US
dc.contributor.authorYin, Wotaoen_US
dc.contributor.funderNational Science Foundationen_US
dc.contributor.funderOffice of Naval Researchen_US
dc.contributor.funderAlfred P. Sloan Foundationen_US
dc.date.accessioned2013-07-11T14:56:46Zen_US
dc.date.available2013-07-11T14:56:46Zen_US
dc.date.issued2012-07-03en_US
dc.description.abstractWe 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.en_US
dc.embargo.termsnoneen_US
dc.identifier.citationGuo, Weihong and Yin, Wotao. "Edge Guided Reconstruction for Compressive Imaging." <i>SIAM Journal on Imaging Sciences,</i> 5, no. 3 (2012) Society for Industrial and Applied Mathematics: 809-834. http://dx.doi.org/10.1137/110837309.en_US
dc.identifier.doihttp://dx.doi.org/10.1137/110837309en_US
dc.identifier.grantIDCAREER award DMS-07-48839 (National Science Foundation)en_US
dc.identifier.grantIDN00014- 08-1-1101 (Office of Naval Research)en_US
dc.identifier.grantIDResearch Fellowship (Alfred P. Sloan Foundation)en_US
dc.identifier.urihttps://hdl.handle.net/1911/71530en_US
dc.language.isoengen_US
dc.publisherSociety for Industrial and Applied Mathematicsen_US
dc.subject.keywordcompressive sensingen_US
dc.subject.keywordedge detectionen_US
dc.subject.keywordtotal variationen_US
dc.subject.keyworddiscrete Fourier transformen_US
dc.subject.keywordmagnetic resonance imagingen_US
dc.titleEdge Guided Reconstruction for Compressive Imagingen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
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