Compressed Sensing Inspired Image Reconstruction from Overlapped Projections

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dc.contributor.authorYang, Linen
dc.contributor.authorLu, Yangen
dc.contributor.authorWang, Geen
dc.contributor.departmentSchool of Biomedical Engineering and Sciencesen
dc.date.accessioned2017-09-18T09:58:12Zen
dc.date.available2017-09-18T09:58:12Zen
dc.date.issued2010-06-22en
dc.date.updated2017-09-18T09:58:12Zen
dc.description.abstractThe key idea discussed in this paper is to reconstruct an image from overlapped projections so that the data acquisition process can be shortened while the image quality remains essentially uncompromised. To perform image reconstruction from overlapped projections, the conventional reconstruction approach (e.g., filtered backprojection (FBP) algorithms) cannot be directly used because of two problems. First, overlapped projections represent an imaging system in terms of summed exponentials, which cannot be transformed into a linear form. Second, the overlapped measurement carries less information than the traditional line integrals. To meet these challenges, we propose a compressive sensing-(CS-) based iterative algorithm for reconstruction from overlapped data. This algorithm starts with a good initial guess, relies on adaptive linearization, and minimizes the total variation (TV). Then, we demonstrated the feasibility of this algorithm in numerical tests.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationLin Yang, Yang Lu, and Ge Wang, “Compressed Sensing Inspired Image Reconstruction from Overlapped Projections,” International Journal of Biomedical Imaging, vol. 2010, Article ID 284073, 8 pages, 2010. doi:10.1155/2010/284073en
dc.identifier.doihttps://doi.org/10.1155/2010/284073en
dc.identifier.urihttp://hdl.handle.net/10919/79039en
dc.language.isoenen
dc.publisherHindawien
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.holderCopyright © 2010 Lin Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleCompressed Sensing Inspired Image Reconstruction from Overlapped Projectionsen
dc.title.serialInternational Journal of Biomedical Imagingen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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