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André Ferrari
Researcher at University of Nice Sophia Antipolis
Publications - 113
Citations - 1712
André Ferrari is an academic researcher from University of Nice Sophia Antipolis. The author has contributed to research in topics: Estimation theory & Iterative reconstruction. The author has an hindex of 21, co-authored 113 publications receiving 1541 citations. Previous affiliations of André Ferrari include Centre national de la recherche scientifique.
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Total coronagraphic extinction of rectangular apertures using linear prolate apodizations
TL;DR: In this article, the authors presented a theoretical study of stellar coronagraphy with apodized entrance apertures, restricted to a perfect telescope operating in space, and a monochromatic on-axis unresolved star.
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Speckle Noise and Dynamic Range in Coronagraphic Images
TL;DR: In this article, the statistical properties of the residual starlight in coronagraphic images and the effect of a coronagraph on the speckle and photon noise were derived and analyzed.
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Apodized Lyot coronagraph for SPHERE/VLT I. Detailed numerical study
Marcel Carbillet,Philippe Bendjoya,Lyu Abe,Géraldine Guerri,Anthony Boccaletti,J.-B. Daban,Kjetil Dohlen,André Ferrari,S. Robbe-Dubois,Richard Douet,Farrokh Vakili +10 more
TL;DR: The Spectro-Polarimetric High-contrast Exoplanet REsearch (SPHERE) is a second-generation Very Large Telescope (VLT) instrument dedicated to high contrast direct imaging of exoplanets which first-light is scheduled for 2011 as mentioned in this paper.
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The study of an iterative method for the reconstruction of images corrupted by Poisson and Gaussian noise
TL;DR: In this paper, the authors proved the existence of solutions of the maximum likelihood problem by investigating the properties of the negative log of the likelihood function, and showed that the iterative method proposed by the above-mentioned authors is a scaled gradient method for the constrained minimization of this function in the closed and convex cone of the nonnegative vectors and that, if it is convergent, the limit is a solution of the constrained ML problem.
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Bivariate Gamma Distributions for Image Registration and Change Detection
TL;DR: Bivariate gamma distributions are good candidates allowing us to develop new image registration algorithms and new change detectors, according to a classical similarity measure which can be used for image registration or change detection.