Bayesian image reconstruction with space-variant noise suppression

In this paper we present a Bayesian image re- construction algorithm with entropy prior (FMAPE) that uses a space-variant hyperparameter. The spatial varia- tion of the hyperparameter allows dierent degrees of res- olution in areas of dierent statistical characteristics, thus avoiding the large residuals resulting from algorithms that use a constant hyperparameter. In the rst implementa- tion of the algorithm, we begin by segmenting a Maximum Likelihood Estimator (MLE) reconstruction. The segmen- tation method is based on using a wavelet decomposition and a self-organizing neural network. The result is a prede- termined number of extended regions plus a small region for each star or bright object. To assign a dierent value of the hyperparameter to each extended region and star, we use either feasibility tests or cross-validation methods. Once the set of hyperparameters is obtained, we carried out the nal Bayesian reconstruction, leading to a recon- struction with decreased bias and excellent visual charac- teristics. The method has been applied to data from the non-refurbished Hubble Space Telescope. The method can be also applied to ground-based images.