Image Reconstruction from Sparse Samples Using Directional Total Variation Minimization

Publication:
Image Reconstruction from Sparse Samples Using Directional Total Variation Minimization

dc.contributor.author	Demircan Türeyen, Ezgi
dc.contributor.author	Kamasak, Mustafa E.
dc.contributor.author	Bayram, İlker
dc.contributor.authorID	237397	tr_TR
dc.contributor.authorID	190955	tr_TR
dc.date.accessioned	2018-07-19T10:37:32Z
dc.date.available	2018-07-19T10:37:32Z
dc.date.issued	2016
dc.description.abstract	This paper considers reconstruction of missing pixels and formulates the problem under directional total variation (DTV) regularization. In order to devise an algorithm, forward-backward splitting method is used as a convex optimization tool, in conjunction with a fast projected gradient-based algorithm. The results are compared with the results of TV-based setting, and the utility of using DTV is shown in terms of accuracy, when an image with a dominant direction is the case.	tr_TR
dc.identifier.isbn	978-1-5090-1679-2
dc.identifier.uri	https://hdl.handle.net/11413/2202
dc.identifier.uri	https://doi.org/10.1109/SIU.2016.7495957
dc.identifier.wos	391250900273
dc.language.iso	en
dc.publisher	IEEE, 345 E 47Th St, New York, Ny 10017 USA
dc.relation	2016 24th Signal Processing and Communication Application Conference (SIU)	tr_TR
dc.subject	image reconstruction	tr_TR
dc.subject	directional total variation	tr_TR
dc.subject	convex optimization	tr_TR
dc.subject	proximal splitting methods	tr_TR
dc.subject	Algorithms	tr_TR
dc.title	Image Reconstruction from Sparse Samples Using Directional Total Variation Minimization	tr_TR
dc.type	Article
dspace.entity.type	Publication
local.indexed.at	WOS