Poisson noise removal via learned dictionary

In this paper, we address the restoration of images corrupted by Poisson noise. The proposed new model contains two terms: one is from the sparse representation of the transformed image via variance stabilizing transformation (VST); the other is a data-fidelity term caused by the statistical properties of Poisson noise. The main algorithm is efficient. We first learn a dictionary to sparsely represent the transformed image using a state-of-the-art dictionary learning method, and then solve the minimization of the variational form by Newton method. Comparative experiments are carried out to show the leading performance of our new model.