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.
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