
	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.

PDF Fulltext XML References Citation Report Citation

RELATED ARTICLES:

•	Detection of Harmonic and Inter-harmonic Caused by Synchronization of Small Hydropower Station

•	Implementation and Comparison of the License Plate Algorithms: A Case Study

•	The New Detection Algorithm for the Grid Accessing Voltage Wave Fluctuation of High Power EPS

•	A Robust and Simple Piecewise Approximation to SαS Distribution with Bi-Region Model

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

COMMENT ON THIS PAPER

Navigation


		Online First
		Current Issue
		Previous Issues
		Editorial Board
		Submit a Manuscript
		Guide to Authors
		Subscribe to E-alerts

Indexed In


		ASCI-Database
		Asian Digital Library
		Google Scholar

Science Alert