  Deep learning (DL) has demonstrated its powerful capabilities in the field of
image inpainting. The DL-based image inpainting approaches can produce visually
plausible results, but often generate various unpleasant artifacts, especially
in the boundary and highly textured regions. To tackle this challenge, in this
work, we propose a new end-to-end, two-stage (coarse-to-fine) generative model
through combining a local binary pattern (LBP) learning network with an actual
inpainting network. Specifically, the first LBP learning network using U-Net
architecture is designed to accurately predict the structural information of
the missing region, which subsequently guides the second image inpainting
network for better filling the missing pixels. Furthermore, an improved spatial
attention mechanism is integrated in the image inpainting network, by
considering the consistency not only between the known region with the
generated one, but also within the generated region itself. Extensive
experiments on public datasets including CelebA-HQ, Places and Paris StreetView
demonstrate that our model generates better inpainting results than the
state-of-the-art competing algorithms, both quantitatively and qualitatively.
The source code and trained models will be made available at
https://github.com/HighwayWu/ImageInpainting.
