"Denoising of imagery for inspection tasks using higher-order statistic" by Samuel Peter Kozaitis
 

Document Type

Conference Proceeding

Publication Title

Proceedings of SPIE - the International Society for Optical Engineering

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.

DOI

10.1117/12.686619

Publication Date

10-12-2006

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