Microtexture Inpainting through Gaussian Conditional Simulation

Abstract : Image inpainting consists in filling missing regions of an image by inferring from the surrounding content. In the case of texture images, inpainting can be formulated in terms of conditional simulation of a stochastic texture model. Many texture synthesis methods thus have been adapted to texture inpainting, but these methods do not offer theoretical guarantees since the conditional sampling is in general only approximate. Here we show that in the case of Gaussian textures, inpainting can be addressed with perfect conditional simulation relying on kriging estimation. We thus obtain a micro-texture inpainting algorithm that is able to fill holes of any shape and size in an efficient manner while respecting exactly a stochastic model.
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Communication dans un congrès
IEEE International Conference on Acoustics, Speech, and Signal Processing, Mar 2016, Shanghai, China
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https://hal.archives-ouvertes.fr/hal-01214695
Contributeur : Lionel Moisan <>
Soumis le : jeudi 18 février 2016 - 17:00:25
Dernière modification le : vendredi 1 février 2019 - 15:50:13

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  • HAL Id : hal-01214695, version 2

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Bruno Galerne, Arthur Leclaire, Lionel Moisan. Microtexture Inpainting through Gaussian Conditional Simulation. IEEE International Conference on Acoustics, Speech, and Signal Processing, Mar 2016, Shanghai, China. 〈hal-01214695v2〉

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