Please use this identifier to cite or link to this item: https://doi.org/10.1109/TPAMI.2012.77
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dc.titleA closed-form solution to retinex with nonlocal texture constraints
dc.contributor.authorZhao, Q.
dc.contributor.authorTan, P.
dc.contributor.authorDai, Q.
dc.contributor.authorShen, L.
dc.contributor.authorWu, E.
dc.contributor.authorLin, S.
dc.date.accessioned2014-10-07T04:22:22Z
dc.date.available2014-10-07T04:22:22Z
dc.date.issued2012
dc.identifier.citationZhao, Q., Tan, P., Dai, Q., Shen, L., Wu, E., Lin, S. (2012). A closed-form solution to retinex with nonlocal texture constraints. IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (7) : 1437-1444. ScholarBank@NUS Repository. https://doi.org/10.1109/TPAMI.2012.77
dc.identifier.issn01628828
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/81844
dc.description.abstractWe propose a method for intrinsic image decomposition based on retinex theory and texture analysis. While most previous methods approach this problem by analyzing local gradient properties, our technique additionally identifies distant pixels with the same reflectance through texture analysis, and uses these nonlocal reflectance constraints to significantly reduce ambiguity in decomposition. We formulate the decomposition problem as the minimization of a quadratic function which incorporates both the retinex constraint and our nonlocal texture constraint. This optimization can be solved in closed form with the standard conjugate gradient algorithm. Extensive experimentation with comparisons to previous techniques validate our method in terms of both decomposition accuracy and runtime efficiency. © 2012 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TPAMI.2012.77
dc.sourceScopus
dc.subjectIntrinsic images
dc.subjectnonlocal constraint
dc.subjectretinex
dc.subjecttexture
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/TPAMI.2012.77
dc.description.sourcetitleIEEE Transactions on Pattern Analysis and Machine Intelligence
dc.description.volume34
dc.description.issue7
dc.description.page1437-1444
dc.description.codenITPID
dc.identifier.isiut000304138300014
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