Paper Info
Title
FIR filter based fuzzy-genetic mixed noise removal
Abstract
In this paper a FIR nonlinear fuzzy filter for image processing, which is most effective in removal of mixed noise, is proposed. In general itpsilas hard to distinguish noise and edges information. This ambiguity leads us to use fuzzy concepts. Fuzzy similarity is used here to suppress noise and preserve edges. Parameters of the membership function are optimized by genetic algorithm approach. Since our problem here is stochastic, traditional optimization algorithms are of no use anymore. Simulation consists of some combination of Gaussian and salt and pepper noises on different classes of images. Results are compared with traditional median and Wiener filters both from subjective and objective points of view.
Year
DOI
Venue
2007
10.1109/ISSPA.2007.4555359
Sharjah
Keywords
Field
DocType
FIR filters,edge detection,fuzzy set theory,genetic algorithms,image denoising,FIR filter,edge detection,fuzzy-genetic mixed noise removal,genetic algorithm,image processing
Wiener filter,Computer vision,Median filter,Pattern recognition,Edge detection,Computer science,Fuzzy logic,Salt-and-pepper noise,Fuzzy set,Artificial intelligence,Membership function,Gaussian noise
Conference
ISBN
Citations 
PageRank 
978-1-4244-1779-8
2
0.41
References 
Authors
3
2
Name
Order
Citations
PageRank
Safari, M.S.120.41
Aghagolzadeh, A.292.44