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Proceedings Paper

Improved denoising approach using higher-order statistics
Author(s): Samuel P. Kozaitis
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Paper Abstract

We presented a method to reduce noise in signals using a higher-order, correlation-based approach. This paper examines the differences between hard and soft thresholds using the higher-order method, and the use of different wavelets in the denoising algorithm. Using a detection algorithm derived from third-order statistics, we determined if a wavelet coefficient was either mostly noise or mostly signal based on third-order statistics. We found that hard thresholding worked best when compared to soft thresholding but there is the possibility of improvement using soft thresholding.

Paper Details

Date Published: 9 April 2007
PDF: 9 pages
Proc. SPIE 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V, 657603 (9 April 2007); doi: 10.1117/12.718704
Show Author Affiliations
Samuel P. Kozaitis, Florida Institute of Technology (United States)


Published in SPIE Proceedings Vol. 6576:
Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V
Harold H. Szu; Jack Agee, Editor(s)

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