Edge-preserving wavelet thresholding for image denoising

In this paper we consider a general setting for wavelet based image denoising methods. In fact, in both deterministic regularization methods and stochastic maximum a posteriori estimations, the denoised image f^ is obtained by minimizing a functional, which is the sum of a data fidelity term and a regularization term that enforces a roughness penalty on the solution. The latter is usually defined as a sum of potentials, which are functions of a derivative of the image. By considering particular families of dyadic wavelets, we propose the use of new potential functions, which allows us to preserve and restore important image features, such as edges and smooth regions, during the wavelet denoising process. Numerical results are presented, showing the optimal performance of the denoising algorithm obtained.