The goal is to combine multiple astronomical images of the same field of view into a single model, within the Virtual Observatory framework where the huge amounts of data often exhibit some redundancy. To achieve this goal, we propose to develop a multi-source data fusion method using probability theory. We want to infer an image from several blurred and noisy observations, possibly from different sensors and instruments under various conditions. We aim at the recovery of a compound object "image+uncertainties" that contains a maximum of useful information from the initial data set. In some cases, conserving information may require achieving superresolution. We propose to use a Bayesian inference scheme to invert a generative model that explains the image formation for each observation while taking into account a priori knowledge. Understanding the image formation process is crucial. The originality of the work is in devising a new technique of multi-image data fusion that also addresses spatial super-resolution and recursive model updating. This involves both automatic registration and resampling, which are difficult inverse problems that are treated within a probabilistic framework. Our contribution outperforms state of the art methods in astronomy since it can handle different instrument characteristics for each input and provide uncertainty estimates as well.
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