Astron. Astrophys. Suppl. Ser. 147, 139-149
A combined approach for object detection and deconvolution
J.-L. Starck1,2 -
A. Bijaoui3 -
I. Valtchanov2 -
Send offprint request: J.-L. Starck, e-mail: firstname.lastname@example.org
Statistics Department, Stanford University, Sequoia Hall, Stanford, CA 94305, U.S.A.
SEI-SAP/DAPNIA, CEA-Saclay, 91191 Gif-sur-Yvette Cedex, France
Observatoire de la Côte d'Azur, BP. 229, 06394 Nice Cedex 4, France
School of Computer Science, Queen's University of Belfast, Belfast BT7 1NN, Northern Ireland
Observatoire Astronomique, 11 rue de l'Université, 67000 Strasbourg, France
Received July 7; accepted August 29, 2000
The Multiscale Vision Model is a recent object detection method, based
on the wavelet transform. It allows us to extract
all objects contained in an image, whatever their size or their shape.
From each extracted object, information concerning flux or shape
can easily be determined. We show that such an approach can be combined
with deconvolution, leading to the reconstruction of deconvolved
We discuss the advantages of this approach, such as how we can
perform deconvolution with a space-variant point spread function.
We present a range of examples and applications, in the framework
of the ISO, XMM and other projects, to illustrate the
effectiveness of this approach.
Key words: methods: data analysis -- techniques: image processing
Copyright The European Southern Observatory (ESO)