  Image inpainting techniques have shown promising improvement with the
assistance of generative adversarial networks (GANs) recently. However, most of
them often suffered from completed results with unreasonable structure or
blurriness. To mitigate this problem, in this paper, we present a one-stage
model that utilizes dense combinations of dilated convolutions to obtain larger
and more effective receptive fields. Benefited from the property of this
network, we can more easily recover large regions in an incomplete image. To
better train this efficient generator, except for frequently-used VGG feature
matching loss, we design a novel self-guided regression loss for concentrating
on uncertain areas and enhancing the semantic details. Besides, we devise a
geometrical alignment constraint item to compensate for the pixel-based
distance between prediction features and ground-truth ones. We also employ a
discriminator with local and global branches to ensure local-global contents
consistency. To further improve the quality of generated images, discriminator
feature matching on the local branch is introduced, which dynamically minimizes
the similarity of intermediate features between synthetic and ground-truth
patches. Extensive experiments on several public datasets demonstrate that our
approach outperforms current state-of-the-art methods. Code is available at
https://github.com/Zheng222/DMFN.
