A wavelet-based image denoising technique using spatial priors

We propose a new wavelet-based method for image denoising that applies the Bayesian framework, using prior knowledge about the spatial clustering of the wavelet coefficients. Local spatial interactions of the wavelet coefficients are modeled by adopting a Markov Random Field model. An iterative updating technique known as iterated conditional modes (ICM) is applied to estimate the binary masks containing the positions of those wavelet coefficients that represent the useful signal in each subband. For each wavelet coefficient a shrinkage factor is determined, depending on its magnitude and on the local spatial neighbourhood in the estimated mask. We derive analytically a closed form expression for this shrinkage factor.