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Wasserstein Loss for Image Synthesis and Restoration
2016
SIAM Journal of Imaging Sciences
This paper presents a novel variational approach to impose statistical constraints to the output of both image generation (to perform typically texture synthesis) and image restoration (for instance to achieve denoising and super-resolution) methods. The empirical distributions of linear or non-linear descriptors are imposed to be close to some input distributions by minimizing a Wasserstein loss, i.e. the optimal transport distance between the distributions. We advocate the use of a
doi:10.1137/16m1067494
fatcat:vrbtvkvbxfdrplr42sk632ob5i