An Alternative Single-Image Super Resolution Framework Employing High Frequency Prediction Using A Robust Huber Rational Function

au.link.externalLink [Full Text] (http://ieeexplore.ieee.org/document/7439537/)
dc.contributor.author Kornkamol Thakulsukannant
dc.contributor.author Vorapoj Patanavijit
dc.contributor.other Vincent Mary School of Engineering
dc.date.accessioned 2018-06-08T03:37:24Z
dc.date.available 2018-06-08T03:37:24Z
dc.date.issued 2015-11
dc.description.abstract In general prospective, SI-SR or Single-Image Super-Resolution, which is one of the most useful algorithms of Super Resolution-Reconstruction (SRR) algorithms, is a mathematical procedure for acquiring a high-resolution image from only one coarse-resolution image, which is usually computed by Digital Image Processing (DIP). Even thought there have been substantially researched during the last decade, Single - Image Super-Resolution for applying on real implementations still keeps throw down the gauntlet. One of the practical Single- Image Super-Resolution is the resolution enhancement using prediction of the high-frequency image because of its high performance and its less comple xity however the rational function C(x, y) of high-frequency image prediction process of this technique is depend upon three parameters (b, h, k) therefore the parameter turning is difficult for maximizing its performance. From this problem prospective, this paper presents the alternative SI-SR framework employing robust rational function based on Huber function, which is depend upon only one parameter (T), instead of three parameters like the rational function C(x,y). Using up to fourteen standard images, which are crooked by varied noise models, in analysis testing section, the proposed SI-SR is demonstrated to be somewhat simper than the original SI-SR with equivalent efficiency because the saving in parameter turning time will be very important for SI-SR in real implementations. en_US
dc.format.extent 4 pages en_US
dc.format.mimetype application/pdf en_US
dc.identifier.citation ICIIBMS 2015, Track3: Bioinformatics, Medical Imaging and Neuroscience, Okinawa, Japan, 351-354 en_US
dc.identifier.uri https://repository.au.edu/handle/6623004553/21015
dc.language.iso eng en_US
dc.rights.holder Kornkamol Thakulsukanant en_US
dc.rights.holder Vorapoj Patanavijit
dc.subject SRR (Super Resolution-Reconstruction) en_US
dc.subject SI-SR (Single-Image Super-Resolution) en_US
dc.subject Digital image reconstruction en_US
dc.title An Alternative Single-Image Super Resolution Framework Employing High Frequency Prediction Using A Robust Huber Rational Function en_US
dc.type Text en_US
mods.genre Conference Paper en_US
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