Translation Invariant Wavelet Denoising of Poisson Data

摘要

In this paper, we propose a novel image denoising algorithm to reduce Poisson noise based on state-of-the-art wavelet domain statistical models. We propose an efficient method to estimate the model parameters from the observations, and solve the optimization problem in or-thonormal and translation invariant (TI) wavelet domains. It is observed that TI wavelet transforms produce better estimation performance than orthogonal wavelet transforms. We briefly explain this observation from the viewpoint of approximation theory and minimax estimation theory.