Wavelet Denoising vs ICA denoising for functional optical imaging

被引：11
|
作者：
Emir, UE [1 ]
Akgül, CB [1 ]
Akin, A [1 ]
Ertüzün, A [1 ]
Sankur, B [1 ]
Harmanci, K [1 ]
机构：
[1] Bogazici Univ, Inst Biomed Engn, Istanbul, Turkey
来源：
1ST INTERNATIONAL IEEE EMBS CONFERENCE ON NEURAL ENGINEERING 2003, CONFERENCE PROCEEDINGS | 2003年
关键词：
functional optical imaging; wavelet denoising; independent component analysis;
D O I：
10.1109/CNE.2003.1196841
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：384 / 387
页数：4
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