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dc.contributor.authorKozaitis, Samuel P.
dc.date.accessioned2017-06-06T16:14:59Z
dc.date.available2017-06-06T16:14:59Z
dc.date.issued2006-10-12
dc.identifier.citationKozaitis, S. P. (2006). Denoising of imagery for inspection tasks using higher-order statistics. Paper presented at the Proceedings of SPIE - the International Society for Optical Engineering, 6383 doi:10.1117/12.686619en_US
dc.identifier.urihttp://hdl.handle.net/11141/1471
dc.description.abstractWe 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.en_US
dc.language.isoen_USen_US
dc.rightsThis published article is made available in accordance with publishers policy. It may be subject to U.S. copyright law.en_US
dc.rights.urihttp://spie.org/publications/journals/guidelines-for-authors#Terms_of_Useen_US
dc.titleDenoising of imagery for inspection tasks using higher-order statisticsen_US
dc.typeConference Proceedingen_US
dc.identifier.doi10.1117/12.686619


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