We present an intelligent technique for image
denoising problem of gray level images degraded with
Gaussian white noise in spatial domain. The proposed
technique consists of using fuzzy logic as a mapping
function to decide whether a pixel needs to be krigged
or not. Genetic programming is then used to evolve an
optimal pixel intensity-estimation function for
restoring degraded images. The proposed system has
shown considerable improvement when compared both
qualitatively and quantitatively with the adaptive
Wiener filter, methods based on fuzzy kriging, and a
fuzzy-based averaging technique. Experimental results
conducted using an image database confirms that the
proposed technique offers superior performance in terms
of image quality measures. This also validates the use
of hybrid techniques for image restoration.
%0 Journal Article
%1 Chaudhry:2007:IJIST
%A Chaudhry, Asmatullah
%A Khan, Asifullah
%A Ali, Asad
%A Mirza, Anwar M.
%D 2007
%J International Journal of Imaging Systems and
Technology
%K SSIM, adaptive algorithms, filtering fuzzy genetic image index kriging, logic, measure, programming, punctual restoration, similarity spatial structure
%N 4
%P 224--231
%R doi:10.1002/ima.20105
%T A hybrid image restoration approach: Using fuzzy
punctual kriging and genetic programming
%V 17
%X We present an intelligent technique for image
denoising problem of gray level images degraded with
Gaussian white noise in spatial domain. The proposed
technique consists of using fuzzy logic as a mapping
function to decide whether a pixel needs to be krigged
or not. Genetic programming is then used to evolve an
optimal pixel intensity-estimation function for
restoring degraded images. The proposed system has
shown considerable improvement when compared both
qualitatively and quantitatively with the adaptive
Wiener filter, methods based on fuzzy kriging, and a
fuzzy-based averaging technique. Experimental results
conducted using an image database confirms that the
proposed technique offers superior performance in terms
of image quality measures. This also validates the use
of hybrid techniques for image restoration.
@article{Chaudhry:2007:IJIST,
abstract = {We present an intelligent technique for image
denoising problem of gray level images degraded with
Gaussian white noise in spatial domain. The proposed
technique consists of using fuzzy logic as a mapping
function to decide whether a pixel needs to be krigged
or not. Genetic programming is then used to evolve an
optimal pixel intensity-estimation function for
restoring degraded images. The proposed system has
shown considerable improvement when compared both
qualitatively and quantitatively with the adaptive
Wiener filter, methods based on fuzzy kriging, and a
fuzzy-based averaging technique. Experimental results
conducted using an image database confirms that the
proposed technique offers superior performance in terms
of image quality measures. This also validates the use
of hybrid techniques for image restoration.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Chaudhry, Asmatullah and Khan, Asifullah and Ali, Asad and Mirza, Anwar M.},
biburl = {https://www.bibsonomy.org/bibtex/2c537658326d287418ed08e6c1f67f062/brazovayeye},
doi = {doi:10.1002/ima.20105},
interhash = {972c75d7da26ce0c3d5f41b112a9ae6f},
intrahash = {c537658326d287418ed08e6c1f67f062},
issn = {1098-1098},
journal = {International Journal of Imaging Systems and
Technology},
keywords = {SSIM, adaptive algorithms, filtering fuzzy genetic image index kriging, logic, measure, programming, punctual restoration, similarity spatial structure},
number = 4,
pages = {224--231},
timestamp = {2008-06-19T17:37:35.000+0200},
title = {A hybrid image restoration approach: Using fuzzy
punctual kriging and genetic programming},
volume = 17,
year = 2007
}