Deconvolution of multiple images with high dynamic range and an application to LBT LINC-NIRVANA

Context. The standard Richardson-Lucy method (RLM) does not work well in the deconvolution of astronomical images containing objects with very different angular scales and magnitudes. Therefore, modifications of RLM, applicable to this kind of objects, must be investigated. Aims. We recently proposed a regularization of RLM which provides satisfactory results in the case of particular test objects with high dynamic range. In this paper we extend this method to the case of multiple image deconvolution, having in mind application to the reconstruction of the images provided by Fizeau interferometers such as LINC-NIRVANA, the German-Italian beam combiner for the Large Binocular Telescope. Methods. RLM is an iterative method for the minimization of the Csiszar divergence, a problem equivalent to maximum likelihood estimation in the case of photon noise. In our approach, the problem is regularized by adding a suitable penalization term to the Csiszar divergence and an iterative method converging to the minimum of the resulting functional is derived from the so-called split gradient method (SGM). Results. The method is tested on a model of young binary star consisting of a core binary surrounded by a dusty circumbinary ring. The results are quite good in the case of exact knowledge of the point spread functions (PSF). However, in the case of approximate knowledge of the PSFs, the accuracy of the reconstruction depends on the difference of magnitude between the ring and the central binary.