Abstract: The paper advocates a statistical approach to image denoising based on a Maximum a Posteriori (MAP) estimation in wavelet domain. In this framework, a new class of independent identically distributed (i.i.d.) stochastic image priors is considered to obtain a simple and tractable solution in a close analytical form. The proposed prior model is considered in the form of Student distribution. The experimental results demonstrate the high fidelity of this model for approximation of the sub-band distributions of wavelet coefficients. The obtained solution is presented in the form of well-studied shrinkage functions.
Key words: Wavelet, image model, image denoising, maximum posterior estimation, Student distribution, shrinkage function.