Both Spatial and Frequency Cues Contribute to High-Fidelity Image
  Inpainting
Ze Lu Yalei Lv Wenqi Wang Pengfei Xiong
Deep generative approaches have obtained great success in image inpainting
recently. However, most generative inpainting networks suffer from either
over-smooth results or aliasing artifacts. The former lacks high-frequency
details, while the latter lacks semantic structure. To address this issue, we
propose an effective Frequency-Spatial Complementary Network (FSCN) by
exploiting rich semantic information in both spatial and frequency domains.
Specifically, we introduce an extra Frequency Branch and Frequency Loss on the
spatial-based network to impose direct supervision on the frequency
information, and propose a Frequency-Spatial Cross-Attention Block (FSCAB) to
fuse multi-domain features and combine the corresponding characteristics. With
our FSCAB, the inpainting network is capable of capturing frequency information
and preserving visual consistency simultaneously. Extensive quantitative and
qualitative experiments demonstrate that our inpainting network can effectively
achieve superior results, outperforming previous state-of-the-art approaches
with significantly fewer parameters and less computation cost. The code will be
released soon.