Learning Sparse Masks for Diffusion-based Image Inpainting. (arXiv:2110.02636v2 [eess.IV] UPDATED)

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 …

arxiv learning masks