1 Introduction

The Large Binocular Telescope (LBT) will consist of two 8.4m mirrors on a common mount. When the two mirrors are coherently cophased, this will work as a total baseline of 22.8m. In several respects, observations with the LBT in this configuration will differ from those with more "conventional'' interferometers. For example, LBT will offer a large field of view, and will permit true imaging by simultaneously measuring all the Fourier components (Angel et al. [1998]).

At the same time, the peculiar point spread function (PSF) and its rotation in the sky due to the alt-azimuthal mount, will require specific data acquisition algorithms, and specialized treatment in the data reduction process. With these points in mind, we started a project to perform tests and develop relevant software. The aim was to investigate the process of image formation and reconstruction at the LBT, taking into account the characteristics of the atmosphere, the telescope performance and adding realistic estimates for the detector read-out noise (RON).

Previously, at least two others groups of authors have already discussed LBT image reconstruction. However, only numerical simulations, based on different reconstruction techniques, have been carried out up to date. Reinheimer et al. ([1997]) have applied the so-called iterative building block method (bispectral analysis) to some point-like and extended objects. They presented a reconstruction method to apply to a speckle utilisation of LBT, with the known limitation in sensitivity of this observation mode. They complemented their simulations with a laboratory experiment. However, this latter was carried out under very favorable turbulence conditions (r₀=2m) and unspecified brightness of the source. Prior to this Hege et al. ([1995]) had explored the use of iterative blind deconvolution (IBD algorithm of Jefferies & Christou [1993]), on simulated LBT images of an extended object.

On the contrary, our work is based on real LBT-like data. In Sect.2 we present the algorithm of reconstruction that we have used and modified. It is based on the Lucy-Richardson deconvolution algorithm (Richardson [1972]; Lucy [1974]), widely used in standard image restoration methods. Section3 shows the potential of this reconstruction technique on some preliminary tests performed on simulated point-like and extended objects.

In Sect.4 we present the experiment realized at the TIRGO observatory. The measurements allowed us to study some quantitative aspects concerning the process of image formation under low-order atmospheric degradation conditions (see Sect.4.1). Section4.2 shows the result of the application of the reconstruction method on LBT-like data from the TIRGO experiment.