Denoising of imagery for inspection tasks using higher-order statistics

Samuel P. Kozaitis

doi:10.1117/12.686619

12 October 2006 Denoising of imagery for inspection tasks using higher-order statistics

Samuel P. Kozaitis

Proceedings Volume 6383, Wavelet Applications in Industrial Processing IV; 63830J (2006) https://doi.org/10.1117/12.686619
Event: Optics East 2006, 2006, Boston, Massachusetts, United States

Abstract

We reduced noise in images using a higher-order, correlation-based method. In this approach, wavelet coefficients were classified as either mostly noise or mostly signal based on third-order statistics. Because the higher than second-order moments of the Gaussian probability function are zero, the third-order correlation coefficient may not have a statistical contribution from Gaussian noise. Using a detection algorithm derived from third-order statistics, we determined if a wavelet coefficient was noisy by looking at its third-order correlation coefficient. Using imagery of space shuttle tiles, our results showed that the minimum mean-squared error obtained using third-order statistics was often less than that using second-order statistics.

Citation Download Citation

Samuel P. Kozaitis "Denoising of imagery for inspection tasks using higher-order statistics", Proc. SPIE 6383, Wavelet Applications in Industrial Processing IV, 63830J (12 October 2006); https://doi.org/10.1117/12.686619

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