POISSON-HAAR TRANSFORM: A NONLINEAR MULTISCALE REPRESENTATION FOR PHOTON-LIMITED IMAGE DENOISING

We present a novel multiscale image representation belonging to the class of multiscale multiplicative decompositions, which we term Poisson-Haar transform. The proposed representation is well-suited for analyzing images degraded by signal-dependent Poisson noise, allowing efficient estimation of their underlying intensity by means of multiscale Bayesian schemes. The Poisson-Haar decomposition has a direct link to the standard 2-D Haar wavelet transform, thus retaining many of the properties that have made wavelets successful in signal processing and analysis. The practical relevance and ef-fectiveness of the proposed approach is verified through denoising experiments on simulated and real-world photon-limited images