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Full-Text Articles in Engineering
Denoising Using Adaptive Thresholding And Higher Order Statistics, Samuel Peter Kozaitis, Tim Young
Denoising Using Adaptive Thresholding And Higher Order Statistics, Samuel Peter Kozaitis, Tim Young
Electrical Engineering and Computer Science Faculty Publications
We showed that a hard threshold for wavelet denoising based on higher order statistics is comparable to a second order soft threshold. The hard threshold can be made adaptive by using a third order statistic as an estimate of the noise. In addition, the relationship between an adaptive hard threshold and retaining a fraction of wavelet coefficients is shown. Qualitative and quantitative metrics based on the mean-squared error are used to compare the hard thresholding and a soft-thresholding technique, BayesShrink.
Thresholding For Higher-Order Statistical Denoising, Samuel Peter Kozaitis, Tim Young
Thresholding For Higher-Order Statistical Denoising, Samuel Peter Kozaitis, Tim Young
Electrical Engineering and Computer Science Faculty Publications
Hard thresholding seems to work well for denoising signals using higher-order statistics. We statistically examined the best values for hard thresholding and related this to the fraction of wavelet coefficients set to zero to obtain the minimum MSE. In addition, we found that the minimum MSE obtained was less sensitive to the threshold when implemented based on a third-order parameter rather than the noise power. Alternatively, we found that this approach to thresholding could be implemented by setting a fixed fraction of wavelet coefficients to zero.
Improved Denoising Approach Using Higher-Order Statistics, Samuel Peter Kozaitis
Improved Denoising Approach Using Higher-Order Statistics, Samuel Peter Kozaitis
Electrical Engineering and Computer Science Faculty Publications
We presented a method to reduce noise in signals using a higher-order, correlation-based approach. This paper examines the differences between hard and soft thresholds using the higher-order method, and the use of different wavelets in the denoising algorithm. Using a detection algorithm derived from third-order statistics, we determined if a wavelet coefficient was either mostly noise or mostly signal based on third-order statistics. We found that hard thresholding worked best when compared to soft thresholding but there is the possibility of improvement using soft thresholding.
Denoising Of Imagery For Inspection Tasks Using Higher-Order Statistics, Samuel Peter Kozaitis
Denoising Of Imagery For Inspection Tasks Using Higher-Order Statistics, Samuel Peter Kozaitis
Electrical Engineering and Computer Science Faculty Publications
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 …
Speckle Denoising Using Wavelet Transforms And Higher-Order Statistics, Samuel Peter Kozaitis, Anurat Ingun
Speckle Denoising Using Wavelet Transforms And Higher-Order Statistics, Samuel Peter Kozaitis, Anurat Ingun
Electrical Engineering and Computer Science Faculty Publications
We reduced speckle noise in SAR imagery by retaining only those wavelet coefficients with significant third-order correlation coefficients. These coefficients were generated from the cross-correlation functions of the image and wavelet basis functions. Using this approach, we compared the results between directly applying our denoising method, and first preprocessing by taking the logarithm of an image. In our approach, we examined wavelet coefficients in an environment where the contribution from the second-order moment of the noise had been reduced.
Reduction Of Multiplicative Noise Using Higher-Order Statistics, Samuel Peter Kozaitis, Anurat Ingun, Rufus H. Cofer
Reduction Of Multiplicative Noise Using Higher-Order Statistics, Samuel Peter Kozaitis, Anurat Ingun, Rufus H. Cofer
Electrical Engineering and Computer Science Faculty Publications
We used a higher-order correlation-based method for signal denoising of images corrupted by multiplicative noise. Using the logarithm of an image, we applied a third-order correlation technique for identification of wavelet coefficients that contained mostly signal. In our approach, we examined wavelet coefficients in an environment where the contribution from the second-order moment of the noise had been reduced. Our results compared favorably and were less sensitive to threshold selection when compared to a second-order wavelet denoising method.