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Variational modeling is a powerful framework to address inverse imaging problems that enables to incorporate prior knowledge and to make use of efficient optimization tools, while offering theoretical guaranties. Regularized models based on gradient penalizations are very successful in image processing, such as the popular total variation formulations that have been proposed in the last decades. This symposium aims at giving a representative sample of such recent developments for applications to image and point-cloud restoration and segmentation, with new definitions of the total variation relying on original discrete formulations and adaptive non-local schemes, or combined with local regularity estimation.