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

Haritopoulos, Michel and Yin, Hujin and Allinson, Nigel M. (2002) Image denoising using self-organizing map-based nonlinear independent component analysis. Neural Networks, 15 (8-9). pp. 1085-1098. ISSN 0893-6080

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Item Type:Article
Item Status:Live Archive

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. © 2002 Elsevier Science Ltd. All rights reserved.

Keywords:Blind source separation, Independent component analysis, Self organizing maps, Image denoising, Neural networks, accuracy, artificial neural network, calculation, conference paper, imaging, noise, priority journal, signal processing, simulation, Algorithms, Humans, Neural Networks (Computer), Nonlinear Dynamics, Portraits
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:8569
Deposited On:18 Apr 2013 08:13

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