Wavelet Denoising vs ICA denoising for functional optical imaging

Emir U., Akgul C., Akin A., Ertuzun A., Sankur B., Harmanci K.

1st International IEEE/EMBS Conference on Neural Engineering, CAPRI, Italy, 20 - 22 March 2003, pp.384-387 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/cne.2003.1196841
  • City: CAPRI
  • Country: Italy
  • Page Numbers: pp.384-387
  • Keywords: functional optical imaging, wavelet denoising, independent component analysis
  • Acibadem Mehmet Ali Aydinlar University Affiliated: No

Abstract

We performed a comparison between two source signal extraction algorithms, namely the Wavelet Denoising (WD) by Soft Thresholding and Independent Component Analysis (ICA) on a simulated functional optical imaging data. The simulated data are generated by combining a gamma function superimposed on a very low frequency sine wave as the source data and the additive noise components are chosen as having both Gaussian and non-Gaussian parts. We observed that ICA denoising outperforms significantly wavelet denoising scheme when the signal-to-noise ratio (SNR) decreases to below 0 dB.