Comparative Experimental Exploration of Robust Norm Functions for Iterative Super Resolution Reconstructions under Noise Surrounding
Comparative Experimental Exploration of Robust Norm Functions for Iterative Super Resolution Reconstructions under Noise Surrounding
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2015
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eng
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application/pdf
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9 pages
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ECTI Transactions on EEC (Electrical Engineering/Electronics and Communications), ECTI Association, Thailand, Vol. 13, No. 2, July 2015, 83-91
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Abstract
In DIP (Digital Image Processing) research society,
the multi-frame SRR (Super Resolution Reconstruction)
algorithm has grown to be the momentous
theme in the last ten years because of its
cost e ectiveness and its superior spectacle. Consequently,
for a multi-frame SRR algorithm which
is commonly comprised of a Bayesian ML (Maximum
Likelihood) approach and a regularization technique
into the unify SRR framework, numerous robust
norm functions (which have both redescending
and non-redescending in uence functions) have been
commonly comprised in the unify SRR framework for
increasingly against noise or outlier. First, this paper
presents the mathematical model of several iterative
SRR based on a Bayesian ML (Maximum Likelihood)
approach and a regularization technique. Three
groups of robust norm functions (a zero-redescending
in uence function (Tukey's Biweight, Andrew's Sine
and Hampel), a nonzero-redescending in uence function
(Lorentzian, Leclerc, Geman&McClure, Myriad
and Meridian) and a non-redescending in uence function
(Huber)) are mathematically incorporated into
the SRR framework. The close form solutions of the
SRR framework based on these robust norm functions
have been concluded. Later, the experimental
section utilizes two standard images of Lena and
Susie (40th) for pilot studies and fraudulent noise patterns
of noiseless, AWGN, Poisson, Salt&Pepper, and
Speckle of several magnitudes used to contaminate
these two standard images. In order to acquire the
maximum PSNR, the comparative experimental exploration
has been done by comprehensively tailoring
all experimental parameters such as step-size, regularization
parameter, norm constant parameter.