Wavelet denoising vs. ICA denoising for functional optical imaging

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.