Showing papers by "Francoise Preteux published in 1997"

Generalized support constraint for three-dimensional reconstruction from incomplete Fourier spectra

Abstract: Many imaging systems involve a loss of information that requires the incorporation of prior knowledge in the restoration/reconstruction process. We focus on the typical case of 3D reconstruction from an incomplete set of projections. An approach based on constrained optimization is introduced. This approach provides a powerful mathematical framework for selecting a specific solution from the set of feasible solutions; this is done by minimizing some criteria depending on prior densitometric information that can be interpreted through a generalized support constraint. We propose a global optimization scheme using a deterministic relaxation algorithm based on Bregman's algorithm associated with half-quadratic minimization techniques. When used for 3D vascular reconstruction from 2D digital subtracted angiography (DSA) data, such an approach enables the reconstruction of a well-contrasted 3D vascular network in comparison with results obtained using standard algorithms.

Lossless compression of multispectral SPOT images

Abstract: In this paper, we address the problem of lossless multispectral compression of remote-sensing data acquired using SPOT satellites. Compression algorithms have classically two stages: a transformation of the available data and coding. In the first stage, the aim is to express the spectral data as uncorrelated data in an optimal way. In the second stage, the coding is performed via the use of either a Rice or an arithmetic coding. In the first part of this paper, we discuss two well-known schemes, namely predictive technique and S + P transform, for the spatial decorrelation of multispectral SPOT images. Obviously, using only spatial properties is not optimal. However, few works have been carried out to address simultaneously the three intrinsic dimensions of multispectral images. In order to overcome this limitation, we have developed a predictive model based on three 3D-predictors. Compression ratios obtained are presented and discussed. In particular, there is a significant improvement in the compression ratios with respect to lossless compression methods based on spatial decorrelation method.