Improved denoising approach using higher-order statistics

Samuel P. Kozaitis

doi:10.1117/12.718704

9 April 2007 Improved denoising approach using higher-order statistics

Samuel P. Kozaitis

Proceedings Volume 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V; 657603 (2007) https://doi.org/10.1117/12.718704
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

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.

Citation Download Citation

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); https://doi.org/10.1117/12.718704

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