A Bayesian approach of wavelet based image denoising in a hyperanalytic multi-wavelet context

We propose the use of a new implementation of the hyperanalytic wavelet transform, (HWT), in association with a Maximum a Posteriori (MAP) filter named bishrink. The denoising methods based on wavelets are sensitive to the selection of the mother wavelets. Taking into account the drawbacks of the bishrink filter and the sensitivity with the selection of the mother wavelets we propose a denoising method in two stages in a multi-wavelet context. It is based on diversification followed by wavelet fusion. Some simulation examples and comparisons prove the performances of the proposed method.

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