A Multiscale Vision Model to Analyse Field Astronomical Images

Abstract

We have implemented a multiscale vision model based on the wavelet transform to analyse field astronomical images. The discrete transform is performed by the à trous algorithm. The vision model is based on the notion of significant structures. We identify the pixels of the associated wavelet transform space (WTS) with the objects. For each scale a region labelling is carried out. An interscale connectivity graph is then established. In accordance with some rules that permit false detections to be removed, the objects and their sub-objects are identified. They define respectively trees and sub-trees in the graph. In this way, the identification of the WTS pixels of the tree related to a given object leads to the reconstruction of its image by the conjugate gradient method. The model has been tested successfully on simulated images of stars and galaxies which allow us to show the capabilities of the detection and restoration procedures of the model. Finally, tests on real images show that one can analyse complex structures better than with classical astronomical vision models.