Abstract

We present a Bayesian Voronoi image reconstruction (VIR) technique 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 χ 2 values are used to compare our reconstructions with fixedgrid models. VIR has the advantage of modeling the image with few parameters, obtaining a better image from a Bayesian point of view. © 2008. The American Astronomical Society. All rights reserved.