Intelligent I
Intelligent
Image Restoration Approach: Using Neural Networks to Eradicate Dilemma in
Punctual Kriging
Asmatullah Chaudhry1, 2,
Asifullah Khan3, and Jin Young Kim1, Quan Qi Niu1
1School of Electronics & Computer Engineering, CNU,
Gwangju, South Korea
2HRD, PINSTECH, P.O. Nilore, Islamabad, Pakistan
3Department of Computer & Information Sciences,
PIEAS, Nilore, Islamabad, Pakistan
[email protected], [email protected], [email protected], [email protected]
Abstract: We
report an intelligent image restoration approach by combining the
geostatistical interpolation technique of punctual kriging and the machine
learning approach of adaptive learning. Digital images degraded from Gaussian
white noise are restored by first utilizing fuzzy logic for selecting pixels
that need to be kriged. The concept of punctual kriging is then used to
estimate the intensity of a pixel. Kriging un-biased estimates mostly suffer
from occurrence of negative weights and matrix inversion failure problems.
Approximation is usually used to avoid these problems in punctual kriging based
image restoration. Artificial neural networks (ANN) are employed to minimize
the cost function of the kriging based pixel intensity estimation procedure.
ANN, in merit to analytical methodologies, avoids both matrix inversion failure
and negative weights problems. Experimental results using four hundred and
fifty images and different image qualitative measures show the superiority of
the proposed method against adaptive Weiner filter and existing fuzzy kriging
approaches. This also validates the use of hybrid approaches to image
restoration problem.
[Chaudhry
A, Khan A, Kim JY, Niu QQ. Intelligent Image Restoration
Approach: Using Neural Networks to Eradicate Dilemma in Punctual Kriging. Life Sci J 2013;10(1):1631-1641]
(ISSN: 1097-8135).http://www.lifesciencesite.com. 240
Keywords: Image restoration, Artificial neural networks (ANN),
Punctual kriging, Matrix inversion failure, Negative Weights. Full Text 240
