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Multiscale methods in signal processing for adaptive optics

Abstract : In this thesis, we introduce a new approach to wavefront phase reconstruction in Adaptive Optics (AO) from the low-resolution gradient measurements provided by a wavefront sensor, using a non-linear approach derived from the Microcanonical Multiscale Formalism (MMF). MMF comes from established concepts in statistical physics, it is naturally suited to the study of multiscale properties of complex natural signals, mainly due to the precise numerical estimate of geometrically localized critical exponents, called the singularity exponents. These exponents quantify the degree of predictability, locally, at each point of the signal domain, and they provide information on the dynamics of the associated system. We show that multiresolution analysis carried out on the singularity exponents of a high-resolution turbulent phase (obtained by model or from data) allows a propagation along the scales of the gradients in low-resolution (obtained from the wavefront sensor), to a higher resolution. We compare our results with those obtained by linear approaches, which allows us to offer an innovative approach to wavefront phase reconstruction in Adaptive Optics.
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Submitted on : Tuesday, July 1, 2014 - 1:06:13 AM
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Suman Kumar Maji. Multiscale methods in signal processing for adaptive optics. Other [cs.OH]. Université Sciences et Technologies - Bordeaux I, 2013. English. ⟨NNT : 2013BOR14912⟩. ⟨tel-00909085v2⟩



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