Frédéric Champagnat

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—Iteratively Reweighted Least Squares (IRLS) and Residual Steepest descent (RSD) algorithms of robust statistics arise as special cases of half-quadratic schemes [1]. Here, we adopt a statistical framework and we show that both algorithms are instances of the EM algorithm. The augmented dataset respectively involves a scale and a location mixture of(More)
Super-resolution (SR) techniques make use of subpixel shifts between frames in an image sequence to yield higher resolution images. We propose an original observation model devoted to the case of nonisometric inter-frame motion as required, for instance, in the context of airborne imaging sensors. First, we describe how the main observation models used in(More)
—This paper provides a complete characterization of stationary Markov random fields on a finite rectangular (non-toroidal) lattice in the basic case of a second-order neighborhood system. Equivalently, it characterizes stationary Markov fields on 2 whose restrictions to finite rectangular subsets are still Markovian (i.e., even on the boundaries). Until(More)
Because true Maximum Likelihood (ML) is too expensive, the dominant approach in Bernoulli-Gaussian (BG) myopic deconvolution consists in the joint maximization of a single Generalized Likelihood with respect to the input signal and the hyperparameters. This communication assesses the theoretical properties of a related Maximum Generalized Marginal(More)
Robust estimation of the optical flow is addressed through a multiresolution energy minimization. It involves repeated evaluation of spatial and temporal gradients of image intensity which rely usually on bilinear interpolation and image filtering. We propose to base both computations on a single pyramidal cubic B-spline model of image intensity. We show(More)