  In the image inpainting task, the ability to repair both high-frequency and
low-frequency information in the missing regions has a substantial influence on
the quality of the restored image. However, existing inpainting methods usually
fail to consider both high-frequency and low-frequency information
simultaneously. To solve this problem, this paper proposes edge information and
mask shrinking based image inpainting approach, which consists of two models.
The first model is an edge generation model used to generate complete edge
information from the damaged image, and the second model is an image completion
model used to fix the missing regions with the generated edge information and
the valid contents of the damaged image. The mask shrinking strategy is
employed in the image completion model to track the areas to be repaired. The
proposed approach is evaluated qualitatively and quantitatively on the dataset
Places2. The result shows our approach outperforms state-of-the-art methods.
