Denoising of aerial imagery using higher-order statistics

Authors:
Samuel P. Kozaitis
Affiliations:
Florida Institute of Technology Electrical Engineering, Melbourne, FL
Venue:
SPPRA'06 Proceedings of the 24th IASTED international conference on Signal processing, pattern recognition, and applications
Year:
2006

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Abstract

We reduced noise in aerial imagery using a higher-order correlation-based method using wavelet transforms. In our approach, we separated wavelet coefficients and noise based on a third-order detection algorithm. 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. The extension of denoising using higher-order statistics from 1-D to 2-D is not necessarily straightforward. We investigated different separable approache for image denosing. Using our higher-order statistical approach gave the lowest MSE in all cases when compared to conventional second-order denoising. Averaging the results of row and column processing resulted in the best perceptual quality for our imagery.