We present a Bayesian Voronoi image reconstruction technique (VIR) for
interferometric data. Bayesian analysis applied to the inverse problem allows
us to derive the a-posteriori probability of a novel parameterization of
interferometric images. We use a variable Voronoi diagram as our model in place
of the usual fixed pixel grid. A quantization of the intensity field allows us
to calculate the likelihood function and a-priori probabilities. The Voronoi
image is optimized including the number of polygons as free parameters. We
apply our algorithm to deconvolve simulated interferometric data. Residuals,
restored images and chi^2 values are used to compare our reconstructions with
fixed grid models. VIR has the advantage of modeling the image with few
parameters, obtaining a better image from a Bayesian point of view.Comment: 27 pages, 10 figures, to be published in APJ, 672, 127