Improving de-noising by coefficient de-noising and dyadic wavelet transform
Soft thresholding has been a standard wavelet de-noising procedure in many signal and image processing applications. Theoretically it is also almost optimal in the sense of nearly achieving the minimax mean-squared error. Inspired by this property, the paper proposes the addition of coefficient de-noising before soft thresholding. This extra step serves to reduce noise in the empirical wavelet coefficients at each scale, and can be shown to yield a lower mean-squared error Moreover we advocate the use of the translation-invariant dyadic wavelet transform, together with an approximate self-dual wavelet, instead of the discrete wavelet transform (DWT) in performing denoising. Experiments show that the proposed method improves the signal-to-noise ratios of the de-noised signals. Moreover the de-noised signals do not have artifacts typically associated with DWT-based methods.