Our approach proposed in a previous paper for the reduction of boundary effects in the deconvolution of astronomical images by the RichardsonLucy method (RLM) is extended here to the problem of multiple image deconvolution and applied to the reconstruction of the images of LINC-NIRVANA, the German-Italian beam combiner for the Large Binocular Telescope (LBT). We investigate the multiple image RLM, its accelerated version ordered subsets expectation maximization (OSEM), and the regularized versions of these two methods. In addition we show how the approach can be extended to the iterative space reconstruction algorithm (ISRA), which is an iterative method converging to nonnegative least squares solutions. Numerical simulations indicate that the approach can provide excellent results with a considerable reduction of the boundary effects.

Abstract. In previous papers we proposed methods and software for the restoration of images provided by Fizeau interferometers such as LINC-NIRVANA (LN), the German-Italian beam combiner for the Large Binocular Telescope (LBT). It will provide multiple images of the same target corresponding to different orientations of the baseline. Therefore LN will require routinely the use of multiple-image deconvolution methods in order to produce a unique high-resolution image. As a consequence of the complexity of astronomical images, two kinds of methods will be required: first a quick-look method, namely a method that is computationally efficient, allowing a rapid overview and identification of the object being observed; second an ad hoc method designed for that particular object and as accurate as possible. In this paper we investigate the possibility of using Richardson-Lucy-like (RL-like) methods, namely methods designed for the maximization of the likelihood function in the case of Poisson noise, as possible quick-look methods. To this purpose we propose new techniques for accelerating the Ordered Subsets -Expectation Maximization (OS-EM) method, investigated in our previous papers; moreover, we analyze approaches based on the fusion of the multiple images into a single one, so that one can use single-image deconvolution methods which are presumably more efficient than the multiple-image ones. The results are encouraging and all the methods proposed in this paper have been implemented in our software package AIRY.

Context. The paper is about methods for multiple image deconvolution and their application to the reconstruction of the images acquired by the Fizeau interferometer, denoted LINC-NIRVANA, under development for the Large Binocular Telescope (LBT). The multiple images of the same target are obtained with different orientations of the baseline. Aims. To propose and develop a blind method for dealing with cases where no knowledge or very poor knowledge of the point spread functions (PSF) is available. Methods. The approach is an iterative one where object and PSFs are alternately updated using deconvolution methods related to the standard Richardson-Lucy method. It is basically an extension, to the multiple image case, of iterative blind deconvolution methods proposed in the case of a single image. Results. The method is applied to simulated LBT LINC-NIRVANA images and its limitations are investigated. The algorithm has been implemented in the module BLI of the software package AIRY (Astronomical Image Reconstruction in interferometrY), available under request. The preliminary results we have obtained are promising but an extensive simulation program is still necessary for a full understanding of the applicability of the method in the practice of the reconstruction of LINC-NIRVANA images.

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Abstract. In the framework of the methods we introduced for the restoration of images of Fizeau interferometers such as the Large Binocular Telescope, we propose an algorithm which is able to super-resolve compact stellar objects such as a binary system with an angular separation smaller than the angular resolution of the telescope. The method, which works also in the case of a monolithic mirror, is based on a simple modification of the Richardson-Lucy (RL) method or of the Ordered SubsetsExpectation Maximization (OS-EM) method for image deconvolution. In general, it consists of three steps: the first one requires a large number of RL-iterations, which are used to identify and estimate the domain of the unresolved object; the second one is a RL-restoration initialized with the mask of the domain. These two steps can provide a super-resolved image of the stellar system but the photometry of the stars may not be correct. Therefore their positions are derived from the result of the first two steps while their magnitudes are estimated in a third step by solving a simple least-squares problem. In order to show that the method can work in practice, we use (simulated) adaptive-optics-corrected point spread functions (PSF), both in the case of a monolithic and in the case of a binocular telescope, and we investigate mainly the case of binary systems. We analyze the limitations of the method in evaluating the angular separation and the relative magnitude of the two stars. The results we obtain are quite promising.