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.
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