文档介绍：works 15 (2002) 1085–1098
ate/
2002 Special Issue
Image denoising using anizing map-based nonlinear independent
component analysis
Michel Haritopoulos*, Hujun Yin, Nigel M. Allinson
Department of Electrical Engineering and Electronics, UMIST, . Box 88, Manchester M60 1QD, UK
Abstract
This paper proposes the use of anizing 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, parison 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. q 2002
Elsevier Science Ltd. All rights reserved.
Keywords: anizing maps; ponent analysis; Nonlinear; Image denoising; Multiplicative noise
1. Introduction by a nonlinear transfer channel. These methods are of
limited flexibility as they are often parametrized. On the
One of the increasingly important tools in signal other hand, the second category employs parameter-free
processing is ponent analysis (ICA; methods, which are more useful in representing more
Comon, 1994). This was initially proposed to provide a generic nonlinearities. mon neural technique in
solution to the blind source separation (BSS) problem this second category is the well known anizing
(He´rault, Jutten, & Ans, 1985), namely how to recover a set map (SOM), mainly used for the modelling and
of