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Méthode non-additive intervalliste de super-résolution d'images, dans un contexte semi-aveugle

Abstract : Super-resolution is an image processing technique that involves reconstructing a high resolution image based on one or several low resolution images. This technique appeared in the 1980's in an attempt to artificially increase image resolution and therefore to overcome, algorithmically, the physical limits of an imager.Like many reconstruction problems in image processing, super-resolution is known as an ill-posed problem whose numerical resolution is ill-conditioned. This ill-conditioning makes high resolution image reconstruction qualityvery sensitive to the choice of image acquisition model, particularly to the model of the imager Point Spread Function (PSF).In the panorama of super-resolution methods that we draw, we show that none of the methods proposed in the relevant literature allows properly modeling the fact that the imager PSF is, at best, imprecisely known. At best the deviation between model and reality is considered as being a random variable, while it is not: the bias is systematic.We propose to model scant knowledge on the imager's PSF by a convex set of PSFs. The use of such a model challenges the classical inversion methods. We propose to adapt one of the most popular super-resolution methods, known under the name of "iterative back-projection", to this imprecise representation. The super-resolved image reconstructed by the proposed method is interval-valued, i.e. the value associated to each pixel is a real interval. This reconstruction turns out to be robust to the PSF model and to some other errors. It also turns out that the width of the obtained intervals quantifies the reconstruction error.
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Farès Graba. Méthode non-additive intervalliste de super-résolution d'images, dans un contexte semi-aveugle. Traitement du signal et de l'image [eess.SP]. Université Montpellier, 2015. Français. ⟨NNT : 2015MONTS198⟩. ⟨tel-02080798⟩

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