  Image completion has achieved significant progress due to advances in
generative adversarial networks (GANs). Albeit natural-looking, the synthesized
contents still lack details, especially for scenes with complex structures or
images with large holes. This is because there exists a gap between low-level
reconstruction loss and high-level adversarial loss. To address this issue, we
introduce a perceptual network to provide mid-level guidance, which measures
the semantical similarity between the synthesized and original contents in a
similarity-enhanced space. We conduct a detailed analysis on the effects of
different losses and different levels of perceptual features in image
completion, showing that there exist complementarity between adversarial
training and perceptual features. By combining them together, our model can
achieve nearly seamless fusion results in an end-to-end manner. Moreover, we
design an effective lightweight generator architecture, which can achieve
effective image inpainting with far less parameters. Evaluated on CelebA Face
and Paris StreetView dataset, our proposed method significantly outperforms
existing methods.
