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

Michel Haritopoulos, Hujun Yin, Nigel M. Allinson

    Research output: Contribution to journalArticlepeer-review


    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.
    Original languageEnglish
    Pages (from-to)1085-1098
    Number of pages13
    JournalNeural Networks
    Issue number8-9
    Publication statusPublished - Oct 2002


    • Image denoising
    • Independent component analysis
    • Multiplicative noise
    • Nonlinear
    • Self-organizing maps


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