A multi-frame super-resolution method based on the variable-exponent nonlinear diffusion regularizer

dc.contributor.authorMaiseli, Baraka
dc.contributor.authorOgada, Elisha A.
dc.contributor.authorGao, Huijun
dc.date.accessioned2016-07-21T19:17:34Z
dc.date.available2016-07-21T19:17:34Z
dc.date.issued2015-07
dc.description.abstractIn this work, the authors have proposed a multi-frame super-resolution method that is based on the diffusion-driven regularization functional. The new regularizer contains a variable exponent that adaptively regulates its diffusion mechanism depending upon the local image features. In smooth regions, the method favors linear isotropic diffusion, which removes noise more effectively and avoids unwanted artifacts (blocking and staircasing). Near edges and contours, diffusion adaptively and significantly diminishes, and since noise is hardly visible in these regions, an image becomes sharper and resolute—with noise being largely reduced in flat regions. Empirical results from both simulated and real experiments demonstrate that our method outperforms some of the state-of-the-art classical methods based on the total variation frameworken_US
dc.identifier.citationMaiseli, B.J., Elisha, O.A. and Gao, H., 2015. A multi-frame super-resolution method based on the variable-exponent nonlinear diffusion regularizer. EURASIP Journal on Image and Video Processing, 2015(1), pp.1-16.en_US
dc.identifier.doi10.1186/s13640-015-0077-2
dc.identifier.urihttp://hdl.handle.net/20.500.11810/3388
dc.language.isoenen_US
dc.subjectSuper-resolutionen_US
dc.subjectRegularizationen_US
dc.subjectImage reconstructionen_US
dc.subjectDiffusionen_US
dc.titleA multi-frame super-resolution method based on the variable-exponent nonlinear diffusion regularizeren_US
dc.typeJournal Article, Peer Revieweden_US
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