An Alternative Single-Image Super Resolution Framework Employing High Frequency Prediction Using A Robust Huber Rational Function
An Alternative Single-Image Super Resolution Framework Employing High Frequency Prediction Using A Robust Huber Rational Function
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2015-11
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eng
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application/pdf
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4 pages
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Vincent Mary School of Engineering
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ICIIBMS 2015, Track3: Bioinformatics, Medical Imaging and Neuroscience, Okinawa, Japan, 351-354
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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.