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

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

Decomposition of multi-channel intramuscular EMG signals by cyclostationary-based blind source separation

We propose a novel decomposition method for electromyographic (EMG) signals based on blind source separation. Using the cyclostationary properties of motor unit action potential trains (MUAPt), it is shown that MUAPt can be decomposed by joint diagonalization of the cyclic spatial correlation matrix of the observations. After modeling of the source signals, we provide the proof of orthogonality of the sources and of their delayed versions in a cyclostationary context. We tested the proposed method on simulated signals and showed that it can decompose up to 6 sources with a probability of correct detection and classification >95%, using only 8 recording sites. Moreover, we tested the method on experimental multi-channel signals recorded with thin-film intramuscular electrodes, with a total of 32 recording sites. The rate of agreement of the decomposed MUAPt with those obtained by an expert using a validated tool for decomposition was >93%

Separating a real-life nonlinear mixture of images

Abstract. This manuscript presents results obtained using an ICA technique in a real-life nonlinear image separation problem: the separation of the images of the two pages of a paper document when the image from the back page shows through, superimposed on the image of the front page. For this manuscript, two images were printed on opposite sides of a sheet of onion skin paper, and then both sides of the sheet were scanned. The scanned images contained a markedly nonlinear mixture of the original images. Nonlinear ICA, using the MISEP technique, was used to recover the original images. It showed to be able to achieve a reasonable, but not perfect separation. The best results were obtained with a separating system which was somewhat customized, based on prior knowledge about the mixture process, and which used explicit regularization.