Robust Estimators in Fuzzy Logic Cellular-Automata Salt and Pepper Noise Filtering

Nicholas LaVigne; Yusuf Bilgic

doi:10.24297/ijct.v15i1.1712

Authors

Nicholas LaVigne State University of New York at Geneseo
Yusuf Bilgic State University of New York at Geneseo

DOI:

https://doi.org/10.24297/ijct.v15i1.1712

Keywords:

(Nonparametric statistics, Fuzzy Models, Cellular Automata, Image de-noising)

Abstract

In order to increase the performance of computational algorithms in terms of efficiency of estimators, we tested new nonparametric estimators in fuzzy and cellular automata models.Â In particular, image de-noising algorithms exist to restore digital images corrupted by impulse noise.Â These algorithms may do poorly in many common cases, for example, when high contrast and sharp edges lead to outliers, spikes, or non-symmetric patterns for neighborhing pixels.Â This would stem from the choices of estimators in the algorithms. We investigated new nonparametric estimators and compared to existing methods in simulation study. We detected better performances of our new methods under various situations.

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Author Biographies

Nicholas LaVigne, State University of New York at Geneseo

Nicholas LaVigne is a senior undergraduate student in the department of mathematics at the State University of New York at Geneseo. His interests include applied probability, complex systems, and decision theory. He is aspiring to enroll in a PhD program in applied mathematics.

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Yusuf Bilgic, State University of New York at Geneseo

Yusuf Bilgic received his Ph.D. at Western Michigan University (WMU), USA in 2012. Since then, he has been working at State University of New York (SUNY Geneseo) in USA, Department of Mathematics. His current research interests includes nonparametric methods in statistical analysis. He is the author of an R package, rlme, on rank-based analysis of random effects models for hierarchical designs.