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Microtexture Inpainting through Gaussian Conditional Simulation

Citation Author(s):
Arthur Leclaire, Bruno Galerne, Lionel Moisan
Submitted by:
Arthur Leclaire
Last updated:
23 March 2016 - 8:12am
Document Type:
Presentation Slides
Document Year:
Arthur Leclaire
Paper Code:


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 microtexture 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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