Session:

Image Processing and Computer Vision
Tuesday, March 02, 2004, 11.10 – 11.30

Session Chair:

D. Linkens

Paper Title:

Automatic Denoising using Local Independent Component Analysis

Author(s):

P.Gruber, Institute of Biophysics, University of Regensburg, Germany
F.J.Theis, Institute of Biophysics, University of Regensburg, Germany
A.M.Tomé, Dep. Electrónica e telecomunicações, Universidade de Aveiro, Aveiro, Portugal
E.W.Lang, Institute of Biophysics, University of Regensburg, Germany

Abstract:

We present a denoising algorithm for enhancing noisy signals based on local independent component analysis (ICA). We extend a noise reduction algorithm proposed by Vetter et al. by using ICA to separate the signal from the noise. This is done by applying ICA to the signal in localized delayed coordinates. The components resembling noise are detected using estimators of kurtosis or the variance of the autocorrelation. This algorithm can also be applied to the problem of denoising multidimensional data like images or fMRI data sets. In comparison to denoising algorithms using wavelets or Wiener filters the local PCA and ICA algorithms perform considerably better especially the ICA algorithms considering the estimation of higher order statistical moments like kurtosis.