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Image denoising using self-organizing map-based nonlinear independent component analysis.

Michel Haritopoulos·Hujun Yin·Nigel M Allinson
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Abstract

This paper proposes the use of self-organizing maps (SOMs) to the blind source separation (BSS) problem for nonlinearly mixed signals corrupted with multiplicative noise. After an overview of some signal denoising approaches, we introduce the generic independent component analysis (ICA) framework, followed by a survey of existing neural solutions on ICA and nonlinear ICA (NLICA). We then detail a BSS method based on SOMs and intended for image denoising applications. Considering that the pixel intensities of raw images represent a useful signal corrupted with noise, we show that an NLICA-based approach can provide a satisfactory solution to the nonlinear BSS (NLBSS) problem. Furthermore, a comparison between the standard SOM and a modified version, more suitable for dealing with multiplicative noise, is made. Separation results obtained from test and real images demonstrate the feasibility of our approach.

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AlgorithmsHumansNeural Networks, ComputerNonlinear DynamicsComparative StudyJournal ArticlePortraitResearch Support, Non-U.S. Gov't

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Michel Haritopoulos, Hujun Yin, Nigel M Allinson. Image denoising using self-organizing map-based nonlinear independent component analysis.. https://doi.org/10.1016/s0893-6080(02)00081-3

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