É
Éric Thiébaut
Researcher at University of Lyon
Publications - 239
Citations - 5269
Éric Thiébaut is an academic researcher from University of Lyon. The author has contributed to research in topics: Adaptive optics & Iterative reconstruction. The author has an hindex of 37, co-authored 228 publications receiving 4805 citations. Previous affiliations of Éric Thiébaut include École normale supérieure de Lyon & Lyon College.
Papers
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Journal ArticleDOI
Optimal reconstruction for closed-loop ground-layer adaptive optics with elongated spots.
TL;DR: End-to-end simulations of closed-loop ground-layer AO with laser guide stars with improved noise model confirm that, thanks to theImproved noise model, central or side launching of the lasers does not affect the performance with respect to the laser guideStars' flux.
Proceedings ArticleDOI
FRIM: minimum-variance reconstructor with a fractal iterative method
TL;DR: An iterative method using a fractal preconditioning, has recently been suggested for a minimum-variance reconstruction in O(N) operations, and the efficiency of this algorithm for both the open-loop and the closed-loop configurations is analyzed.
Journal ArticleDOI
Phase retrieval from speckle images.
TL;DR: This work proposes an effective algorithm for phase retrieval from a single focused image that makes use of a global optimization strategy and an automatically tuned smoothness prior to overcome local minima and phase degeneracies.
Journal ArticleDOI
Maximum a posteriori planet detection and characterization with a nulling interferometer
Éric Thiébaut,Laurent M. Mugnier +1 more
TL;DR: In this article, a Bayesian method based on the maximum a posteriori (MAP) approach is proposed to solve the problem of reliable planet detection and characterization, which accounts for the noise statistics and optimally combines the data from a nulling interferometer at all observed wavelengths.
Journal ArticleDOI
PACO ASDI: an algorithm for exoplanet detection and characterization in direct imaging with integral field spectrographs
TL;DR: In this article, the authors extend PACO based on local learning of patch covariances in order to capture the spectral and temporal fluctuations of background structures and build a detection algorithm and a spectrum estimation method: PACO ASDI.