Bayesian denoising based on the MAP estimation in wavelet-domain using Bessel K form prior
In this paper, a nonparametric Bayesian estimator in the wavelet domain using the Bessel K form (BKF) distribution will be presented. Our first contribution is to show how the BKF prior is suited to characterize images belonging to Besov spaces. Exploiting this prior, our second contribution is to design a Bayesian L/sub 1/-loss maximum a posteriori estimator nonlinear denoiser, for which we formally establish the mathematical properties. Finally, a comparative study is carried to show the effectiveness of our Bayesian denoiser compared to other denoising approaches.