Thresholding for higher-order statistical denoising

Samuel P. Kozaitis; Tim Young

doi:10.1117/12.777155

3 April 2008 Thresholding for higher-order statistical denoising

Samuel P. Kozaitis, Tim Young

Proceedings Volume 6979, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks VI; 69790O (2008) https://doi.org/10.1117/12.777155
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

Hard thresholding seems to work well for denoising signals using higher-order statistics. We statistically examined the best values for hard thresholding and related this to the fraction of wavelet coefficients set to zero to obtain the minimum MSE. In addition, we found that the minimum MSE obtained was less sensitive to the threshold when implemented based on a third-order parameter rather than the noise power. Alternatively, we found that this approach to thresholding could be implemented by setting a fixed fraction of wavelet coefficients to zero.

Citation Download Citation

Samuel P. Kozaitis and Tim Young "Thresholding for higher-order statistical denoising", Proc. SPIE 6979, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks VI, 69790O (3 April 2008); https://doi.org/10.1117/12.777155

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