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Abstract:
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Many multi-scale approaches to smoothing images with Poisson noise are currently available, including various wavelet methods, Wedgelets, Platelets, and Bayesian multi-scale models. A novel method for smoothing images with Poisson data is introduced, combining existing ideas of Bayesian multi-scale models for smoothing images with a different type of prior that uses the Chinese Restaurant Process as well as a mixture of Dirichlet distributions providing independent samples from the posterior distribution of the underlying intensity. Simulations using various images were conducted. Applications to real astronomical images from the Chandra X-ray observatory are given. This method outperforms many existing methods in its ability to estimate the true intensity image, as well as having other favorable qualities that some methods lack, such as preserving photon flux and fast computation time.
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