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      Learning Sparse Masks for Diffusion-based Image Inpainting

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          Abstract

          Diffusion-based inpainting is a powerful tool for the reconstruction of images from sparse data. Its quality strongly depends on the choice of known data. Optimising their spatial location -- the inpainting mask -- is challenging. A commonly used tool for this task are stochastic optimisation strategies. However, they are slow as they compute multiple inpainting results. We provide a remedy in terms of a learned mask generation model. By emulating the complete inpainting pipeline with two networks for mask generation and neural surrogate inpainting, we obtain a model for highly efficient adaptive mask generation. Experiments indicate that our model can achieve competitive quality with an acceleration by as much as four orders of magnitude. Our findings serve as a basis for making diffusion-based inpainting more attractive for various applications such as image compression, where fast encoding is highly desirable.

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          Author and article information

          Journal
          06 October 2021
          Article
          2110.02636
          0d8ed06b-d9e2-4fd7-89c4-c5d211af79a3

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          eess.IV cs.CV cs.LG

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