Séminaire des doctorants Orléans
Emile Pierret: Stochastic Super-Resolution For Gaussian TexturesEmile Pierret (IDP-Orléans)
Thursday 26 January 2023 11:00 - IDP-Orléans - Salle de Séminaire
Résumé :
I will present the article available at Hal. Super-resolution (SR) is an ill-posed inverse problem which consists in proposing high-resolution images consistent with a given low-resolution one. While most SR algorithms are deterministic, stochastic SR deals with designing a stochastic sampler generating any realistic SR solution. The goal of this paper is to show that stochastic SR is a well-posed and solvable problem when restricting to Gaussian stationary textures. Using Gaussian conditional sampling and exploiting the stationarity assumption, we propose an efficient algorithm based on fast Fourier transform. We also demonstrate the practical relevance of the approach for SR with a reference image. Although limited to stationary microtextures, our approach compares favorably in terms of speed and visual quality to some state of the art methods designed for a larger class of images.
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