  Image completion with large-scale free-form missing regions is one of the
most challenging tasks for the computer vision community. While researchers
pursue better solutions, drawbacks such as pattern unawareness, blurry
textures, and structure distortion remain noticeable, and thus leave space for
improvement. To overcome these challenges, we propose a new StyleGAN-based
image completion network, Spectral Hint GAN (SH-GAN), inside which a carefully
designed spectral processing module, Spectral Hint Unit, is introduced. We also
propose two novel 2D spectral processing strategies, Heterogeneous Filtering
and Gaussian Split that well-fit modern deep learning models and may further be
extended to other tasks. From our inclusive experiments, we demonstrate that
our model can reach FID scores of 3.4134 and 7.0277 on the benchmark datasets
FFHQ and Places2, and therefore outperforms prior works and reaches a new
state-of-the-art. We also prove the effectiveness of our design via ablation
studies, from which one may notice that the aforementioned challenges, i.e.
pattern unawareness, blurry textures, and structure distortion, can be
noticeably resolved. Our code will be open-sourced at:
https://github.com/SHI-Labs/SH-GAN.
