Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency
IEEE Transactions on Signal Processing
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
Translation-invariant denoising using multiwavelets
IEEE Transactions on Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Statistically based multiwavelet denoising
Journal of Computational and Applied Mathematics
Image Denoising Using Three Scales of Wavelet Coefficients
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Image Denoising Using Neighbouring Contourlet Coefficients
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Denoising of three dimensional data cube using bivariate wavelet shrinking
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Rician noise attenuation in the wavelet packet transformed domain for brain MRI
Integrated Computer-Aided Engineering
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The denoising of a natural image corrupted by Gaussian noise is a classical problem in signal or image processing. Donoho and his coworkers at Stanford pioneered a wavelet denoising scheme by thresholding the wavelet coefficients arising from the standard discrete wavelet transform. This work has been widely used in science and engineering applications. However, this denoising scheme tends to kill too many wavelet coefficients that might contain useful image information. In this paper, we propose one wavelet image thresholding scheme by incorporating neighbouring coefficients for both translation-invariant (TI) and non-TI cases. This approach is valid because a large wavelet coefficient will probably have large wavelet coefficients at its neighbour locations. Experimental results show that our algorithm is better than VisuShrink and the TI image denoising method developed by Yu et al. We also investigate different neighbourhood sizes and find that a size of 3 × 3 or 5 × 5 is the best among all window sizes.