Adaptive Charbonnier superresolution method with robust edge preservation capabilities

dc.contributor.authorMaiseli, Baraka J.
dc.contributor.authorLiu, Qiang
dc.contributor.authorElisha, Ogada Achieng
dc.contributor.authorGao, Huijun
dc.date.accessioned2019-08-03T19:13:29Z
dc.date.available2019-08-03T19:13:29Z
dc.date.issued2013-12-16
dc.description.abstractSuperresolution (SR) is known to be an ill-posed inverse problem, which may be solved using some regularization techniques. We have proposed an adaptive regularization method, based on a Charbonnier nonlinear diffusion model to solve an SR problem. The proposed model is flexible because of its automatic capability to reap the strengths of either linear isotropic diffusion, Charbonnier model, or semi-Charbonnier model, depending on the local features of the image. On the contrary, the models proposed from other research works are fixed and hence less feature dependent. This makes such models insensitive to local structures of the images, thereby producing poor reconstruction results. Empirical results obtained from experiments, and presented here, show that the proposed method produces superresolved images which are more natural and contain well-preserved and clearly distinguishable image structures, such as edges. In comparison with other methods, the proposed method demonstrates higher performance in terms of the quality of images it generates.en_US
dc.identifier.doi10.1117/1.JEI.22.4.043027
dc.identifier.urihttp://hdl.handle.net/20.500.11810/5306
dc.language.isoen_USen_US
dc.publisherJournal of Electronic Imagingen_US
dc.subjectSuper-resolution, inverse problem, regularization, Perona-Malik, diffusion, noise removalen_US
dc.titleAdaptive Charbonnier superresolution method with robust edge preservation capabilitiesen_US
dc.typeJournal Articleen_US
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