9 April 2007 Improved denoising approach using higher-order statistics
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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.
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Samuel P. Kozaitis, Samuel P. Kozaitis, } "Improved denoising approach using higher-order statistics", Proc. SPIE 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V, 657603 (9 April 2007); doi: 10.1117/12.718704; https://doi.org/10.1117/12.718704
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