A combined approach for object detection and deconvolution

Free access article

Issue		Astron. Astrophys. Suppl. Ser. Volume 147, Number 1, November II 2000


Page(s)		139 - 149
DOI		http://dx.doi.org/10.1051/aas:2000293

DOI: 10.1051/aas:2000293

Astron. Astrophys. Suppl. Ser. 147, 139-149

A combined approach for object detection and deconvolution

J.-L. Starck^1,2 - A. Bijaoui³ - I. Valtchanov² - F. Murtagh^4,5

Send offprint request: J.-L. Starck, e-mail: jstarck@cea.fr

1 - Statistics Department, Stanford University, Sequoia Hall, Stanford, CA 94305, U.S.A.
2 - SEI-SAP/DAPNIA, CEA-Saclay, 91191 Gif-sur-Yvette Cedex, France
3 - Observatoire de la Côte d'Azur, BP. 229, 06394 Nice Cedex 4, France
4 - School of Computer Science, Queen's University of Belfast, Belfast BT7 1NN, Northern Ireland
5 - Observatoire Astronomique, 11 rue de l'Université, 67000 Strasbourg, France

Received July 7; accepted August 29, 2000

Abstract:

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 objects. 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

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