Denoising using higher-order statistics

Samuel Peter Kozaitis; Sunghee Kim

doi:10.1117/12.485722

1 April 2003 Denoising using higher-order statistics

Samuel Peter Kozaitis, Sunghee Kim

Proceedings Volume 5102, Independent Component Analyses, Wavelets, and Neural Networks; (2003) https://doi.org/10.1117/12.485722
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

We used a higher-order correlation-based method for signal denoising. In our approach, we determined which wavelet coefficients contained mostly noise, or signal, based on higher-order statistics. Because the higher that second-order moments of the Gaussian probability function are zero, the third-order correlation coefficient will not have a statistical contribution from Gaussian noise. We obtained results for both 1-D signals and images. In all cases, our approach showed improved results when compared to a more popular denoising method.

Citation Download Citation

Samuel Peter Kozaitis and Sunghee Kim "Denoising using higher-order statistics", Proc. SPIE 5102, Independent Component Analyses, Wavelets, and Neural Networks, (1 April 2003); https://doi.org/10.1117/12.485722

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