Abstract Virtual Observatories give us access to huge amounts of image data that are often redundant. Our goal is to take advantage of this redundancy by combining images of the same field of view into a single model. To achieve this goal, we propose to develop a multi-source data fusion method that relies on probability and band-limited signal theory. The target object is an image to be inferred from a number of blurred and noisy sources, possibly from different sensors under various conditions (i.e. resolution, shift, orientation, blur, noise...). We aim at the recovery of a compound model "image+uncertainties" that best relates to the observations and contains a maximum of useful information from the initial data set. Thus, in some cases, spatial super-resolution may be required in order to preserve the information. We propose to use a Bayesian inference scheme to invert a forward model, which describes the image formation process for each observation and takes into account some a priori knowledge (e.g. stars as point sources). This involves both automatic registration and spatial resampling, which are ill-posed inverse problems that are addressed within a rigorous Bayesian framework. The originality of the work is in devising a new technique of multi-image data fusion that provides us with super-resolution, self-calibration and possibly model selection capabilities. This approach should outperform existing methods such as resample-and-add or drizzling since it can handle different instrument characteristics for each input image and compute uncertainty estimates as well. Moreover, it is designed to also work in a recursive way, so that the model can be updated when new data become available. Key words: Model-based data fusion, uncertainties, generative models, inverse problems, signal reconstruction, super-resolution, spatial resampling, resolution-limited, B-Splines Email addresses: jalobeanu@lsiit.u-strasbg.fr (A. Jalobeanu, J.A. Gutiérrez), slezak@obs-nice.fr (E. Slezak). URLs: lsiit-miv.u-strasbg.fr/paseo (A. Jalobeanu, J.A. Gutiérrez), www.obs-nice.fr/cassiopee (E. Slezak).

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The UKIRT Wide Field Camera (WFCAM) on Mauna Kea and the VISTA IR mosaic camera at ESO, Paranal, with respectively 4 Rockwell 2kx2k and 16 Raytheon 2kx2k IR arrays on 4m-class telescopes, represent an enormous leap in deep IR survey capability. With combined nightly data-rates of typically 1TB, automated pipeline processing and data management requirements are paramount. Pipeline processing of IR data is far more technically challenging than for optical data. IR detectors are inherently more unstable, while the sky emission is over 100 times brighter than most objects of interest, and varies in a complex spatial and temporal manner. In this presentation we describe the pipeline architecture being developed to deal with the IR imaging data from WFCAM and VISTA, and discuss the primary issues involved in an end-to-end system capable of: robustly removing instrument and night sky signatures; monitoring data quality and system integrity; providing astro-metric and photometric calibration; and generating photon noise-limited images and astronomical catalogues. Accompanying papers by Emerson et al. and Hambly et al. provide an overview of the project and a detailed description of the science archive aspects.