Denoising Method Based on Independent Component Analysis and Its Application to Optical Imaging of Functional Brain

It is a difficult problem to denoise the function optical imaging datum under low Signal Noise Ratio (SNR). The traditional method is filtering denoising. As the noise is wide-band, there remains strong noise in the filtering signal. To resolve this problem, the signal and the noise are regarded as different independent sources, and the independent component analysis (ICA) method is used to separate these independent sources. With the prior information of the signal, we can extract it from the independent sources, so the noise can be sharply reduced. The simulation results show that the ICA denoising performance is obviously superior to the filtering under low SNR.