Impulse noise filtering based on noise-free pixels using genetic programming

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@Article{journals/kais/MajidLMC12,

• author = "Abdul Majid and Choong-Hwan Lee and M. Tariq Mahmood and Tae-Sun Choi",
• title = "Impulse noise filtering based on noise-free pixels using genetic programming",
• journal = "Knowledge and Information Systems",
• year = "2012",
• volume = "32",
• number = "3",
• pages = "505--526",
• publisher = "Springer-Verlag",
• language = "English",
• keywords = "genetic algorithms, genetic programming, Impulse noise, Image restoration, Noise detection, Noise filtering",
• ISSN = "0219-1377",
• DOI = "doi:10.1007/s10115-011-0456-7",
• bibdate = "2012-08-14",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/kais/kais32.html#MajidLMC12",
• size = "22 pages",
• abstract = "Generally, the impulse noise filtering schemes use all pixels within a neighbourhood and increase the size of neighbourhood with the increase in noise density. However, the estimate from all pixels within neighbourhood may not be accurate. Moreover, the larger window may remove edges and fine details as well. In contrast, we propose a novel impulse noise removal scheme that emphasises on few noise-free pixels and small neighbourhood. The proposed scheme searches noise-free pixels within a small neighbourhood. If at least three pixels are not found, then the noisy pixel is left unchanged in current iteration. This iterative process continues until all noisy pixels are replaced with estimated values. In order to estimate the optimal value of the noisy pixel, genetic programming-based estimator is developed. The estimator (function) is composed of useful pixel information and arithmetic functions. Experimental results show that the proposed scheme is capable of removing impulse noise effectively while preserving the fine image details. Especially, our approach has shown effectiveness against high impulse noise density.",

}

Genetic Programming entries for Abdul Majid Choong-Hwan Lee Muhammad Tariq Mahmood Tae Sun Choi

Citations