Abstract
The denoising of a natural signal/image corrupted by Gaussian white noise is a classical problem in signal/image processing. However, it is still in its infancy to denoise high dimensional data. In this paper, we extended Sendur and Selesnick's bivariate wavelet thresholding from two-dimensional image denoising to three dimensional data denoising. Our study shows that bivariate wavelet thresholding is still valid for three dimensional data. Experimental results confirm its superiority. © 2010 Springer-Verlag.
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Chen, G., Bui, T. D., & Krzyzak, A. (2010). Denoising of three dimensional data cube using bivariate wavelet shrinking. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6111 LNCS, pp. 45–51). https://doi.org/10.1007/978-3-642-13772-3_5
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