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A Conditional Multiscale Locally Gaussian Texture Synthesis Algorithm

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Abstract

Exemplar-based texture synthesis is defined as the process of generating, from an input texture sample, new texture images that are perceptually equivalent to the input. In the present work, we model texture self-similarity with conditional Gaussian distributions in the patch space in order to extend the use of stitching techniques. Then, a multiscale texture synthesis algorithm is introduced, where texture patches are modeled at each scale as spatially variable Gaussian vectors in the patch space. The Gaussian distribution for each patch is inferred from the set of its nearest neighbors in the patch space obtained from the input sample. This approach is tested over several real and synthetic texture images, and its results show the effectiveness of the proposed technique for a wide range of textures.

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Acknowledgments

Work partly founded by the European Research Council (advanced Grant Twelve Labors) and the Office of Naval research (ONR Grant N00014-14-1-0023).

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Authors and Affiliations

  1. Centre de Mathématiques et de Leurs Applications, Ecole Normale Supérieure de Cachan, Université Paris-Saclay, 61, avenue du Président Wilson, 94235, Cachan Cedex, France

    Lara Raad, Agnès Desolneux & Jean-Michel Morel

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  1. Lara Raad

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  2. Agnès Desolneux

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  3. Jean-Michel Morel

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Correspondence toLara Raad.

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Raad, L., Desolneux, A. & Morel, JM. A Conditional Multiscale Locally Gaussian Texture Synthesis Algorithm.J Math Imaging Vis56, 260–279 (2016). https://doi.org/10.1007/s10851-016-0656-6

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