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, İtalya, 20 - 22 Mart 2003, ss.384-387 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/cne.2003.1196841
  • Basıldığı Şehir: CAPRI
  • Basıldığı Ülke: İtalya
  • Sayfa Sayıları: ss.384-387
  • Anahtar Kelimeler: functional optical imaging, wavelet denoising, independent component analysis
  • Acıbadem Mehmet Ali Aydınlar Üniversitesi Adresli: Hayır

Özet

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.