Abstract
High-quality image inpainting requires filling missing
regions in a damaged image with plausible content. Existing works either fill the regions by copying image patches or
generating semantically-coherent patches from region context, while neglect the fact that both visual and semantic
plausibility are highly-demanded. In this paper, we propose a Pyramid-context ENcoder Network (PEN-Net) for
image inpainting by deep generative models. The PEN-Net
is built upon a U-Net structure, which can restore an image
by encoding contextual semantics from full resolution input,
and decoding the learned semantic features back into images. Specifically, we propose a pyramid-context encoder,
which progressively learns region affinity by attention from
?This work was performed when the first author was visiting Microsoft
Research as a research intern.