Locally Gaussian exemplar-based texture synthesis

Lara Raad (1) , Agnès Desolneux (1) , J.-M. Morel (1)

1 CMLA - Centre de Mathématiques et de Leurs Applications

The main approaches to texture modeling are the statistical psychophysically inspired model and the patch-based model. In the first model the texture is characterized by a sophisticated statistical signature. The associated sampling algorithm estimates this signature from the example and produces a genuinely different texture. This texture nevertheless often loses accuracy. The second model boils down to a clever copy-paste procedure, which stitches verbatim copies of large regions of the example. We propose in this communication to involve a locally Gaussian texture model in the patch space. It permits to synthesize textures that are everywhere different from the original but with better quality than the purely statistical methods.

Domaines: Applications [stat.AP]

Lara Raad, Agnès Desolneux, J.-M. Morel. Locally Gaussian exemplar-based texture synthesis. 2014. ⟨hal-01023041⟩