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Image Inpainting techniques are generally challenging to evaluate objectively due to the lack of comparative data, as a reference image of the new scene, does not exist.. This paper presents an approach that uses our newly released dataset specifically designed to allow objective evaluation of inpainting techniques. In this work we demonstrate how traditional inpainting techniques can be objectively evaluated and compared together with modern deep learning and adversarial approaches. We further demonstrate how an unsupervised technique compares better than deep learning approaches.
University of Malta, Malta
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