  Although recent inpainting approaches have demonstrated significant
improvements with deep neural networks, they still suffer from artifacts such
as blunt structures and abrupt colors when filling in the missing regions. To
address these issues, we propose an external-internal inpainting scheme with a
monochromic bottleneck that helps image inpainting models remove these
artifacts. In the external learning stage, we reconstruct missing structures
and details in the monochromic space to reduce the learning dimension. In the
internal learning stage, we propose a novel internal color propagation method
with progressive learning strategies for consistent color restoration.
Extensive experiments demonstrate that our proposed scheme helps image
inpainting models produce more structure-preserved and visually compelling
results.
