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

dc.contributor.author	Maiseli, Baraka J.
dc.contributor.author	Elisha, Ogada Achieng
dc.contributor.author	Gao, Huijun
dc.date.accessioned	2019-08-03T18:08:19Z
dc.date.available	2019-08-03T18:08:19Z
dc.date.issued	2015-07-28
dc.description.abstract	In 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 framework.	en_US
dc.identifier.doi	10.1186/s13640-015-0077-2
dc.identifier.uri	http://hdl.handle.net/20.500.11810/5301
dc.language.iso	en_US	en_US
dc.publisher	EURASIP Journal on Image and Video Processing	en_US
dc.subject	Super-resolution, Regularization, Image reconstruction, Diffusion	en_US
dc.title	A multi-frame super-resolution method based on the variable-exponent nonlinear diffusion regularizer	en_US
dc.type	Journal Article	en_US

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