1 Introduction

Astronomical images contain typically a large set of point-like sources (the stars), some quasi point-like objects (faint galaxies, double stars) and some complex and diffuse structures (galaxies, nebulae, planetary stars, clusters, etc.). These objects are often hierarchically organized: star in a small nebula, itself embedded in a galaxy arm, itself included in a galaxy, and so on.

Figure 1: Example of astronomical data: a point source and an extended source are shown, with noise and background. The extended object, which can be detected by eye, is undetected by a standard detection approach

A standard approach to source detection consists of the following four steps:

• Background estimation;
• Noise estimation;
• Detection where flux > Background + $k^{\ast}$ Noise;
• Gaussian or PSF (point source function) fitting to derive the photometry.

Faint extended objects may be lost by this standard approach. Figure 1 shows a typical example where a faint extended object is under the detection limit. In order to detect faint objects, whatever their sizes, Bijaoui (1993) proposed the Multiscale Vision Model (MVM).

A vision model is defined as the sequence of operations required for automated image analysis. Taking into account the scientific purposes, the characteristics of the objects and the existence of hierarchical structures, astronomical images need specific vision models. This is also the case in many other fields, such as remote sensing, hydrodynamic flows, or biological studies. Specific vision models have been implemented for these kinds of images.

We first introduce the MVM as defined in Bijaoui & Rué (1995). Then we describe how deconvolution can be combined with object reconstruction, and show that such an approach improves the photometry of the reconstructed objects, helps with object identification, and can be very helpful for deconvolution with a space-variant PSF.