
	Information Technology Journal
	Year: 2011 \| Volume: 10 \| Issue: 12 \| Page No.: 2260-2267 DOI: 10.3923/itj.2011.2260.2267

A Novel Adaptive Regularized Possibilistic Linear Models Based Median Filter ARBMF for Image Noise Suppression

Hongwei Ge and Wei Song

Abstract: Based on possibility concepts, various Possibilistic Linear Models (PLMs) have been proposed and their pivotal role in fuzzy modeling and the associated applications have been established. The Regularized Possibilistic Linear Model (RPLM) is a regularized version of PLM which can enhance the generalization capability of PLM. In present study, a novel Adaptive RPLMs Based Median Filter (ARBMF) is proposed for improving the performance of median-based filters, preserving image details while effectively suppressing impulsive noises. The proposed filter achieves its effect through the linear combinations of the weighted output of the median filter and the related weighted input signal and the weights are set based on regularized possibilistic linear models concerning the states of the input signal sequence. Experimental results for benchmark images demonstrate that the proposed filter outperforms a number of extensively-used median-based filters. Moreover, the proposed filter also provides excellent robustness with respect to various percentages of impulse noise in our testing examples.

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How to cite this article:

Hongwei Ge and Wei Song, 2011. A Novel Adaptive Regularized Possibilistic Linear Models Based Median Filter ARBMF for Image Noise Suppression. Information Technology Journal, 10: 2260-2267.

DOI: 10.3923/itj.2011.2260.2267

URL: https://scialert.net/abstract/?doi=itj.2011.2260.2267

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