A Fuzzy Genetic Approach to Impulse Noise Removal
Many practical applications require analysis of digital images. An accurate analysis is possible only from an image free of noise. Image denoising with multiple image filters might produce better results than a single filter, but it is very difficult to find a set of appropriate filters and the order in which the filters are to be applied. In this paper, we propose a Fuzzy Genetic Algorithm to find the optimal filter sets for removing impulse noise from images. Here, a Fuzzy Rule Based System is used to adaptively change the crossover probability of the Genetic Algorithm used to determine the optimal sets of filters from a pool of standard image filters. Fuzzy Genetic Algorithm gives better results than conventional Genetic Algorithm. This method does not require any deep knowledge about the image noise factors; so it can be easily used in any image processing application.
KeywordsAdaptive Genetic Algorithm Fuzzy Genetic Algorithm (FGA) Fuzzy Rule Based System (FRBS) Genetic Algorithm (GA) Image filters Impulse noise
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