A Wavelet-Based Image Denoising Technique Using Spatial Priors

We propose a new wavelet-based method for im age 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 coefficientsthat representthe usefulsignal in each subband. For each wavelet coefficientashrinkage factor is determined, depending on its magnitude and on the local spatial neighbourhood in the estim ated m ask. W e derive analytically a closed form expression for this shrinkage factor.

• Type: Conference
• Year: 2000
• Where: ICIP
• Authors: Aleksandra Pizurica, Wilfried Philips, Ignace Lemahieu, Marc Acheroy
• Keywords: ICIP 2000, Image Processing, Local Spatial Neighbourhood, Wavelet Coefficientashrinkage Factor, Wavelet Coefficients