  Image inpainting aims to complete the missing or corrupted regions of images
with realistic contents. The prevalent approaches adopt a hybrid objective of
reconstruction and perceptual quality by using generative adversarial networks.
However, the reconstruction loss and adversarial loss focus on synthesizing
contents of different frequencies and simply applying them together often leads
to inter-frequency conflicts and compromised inpainting. This paper presents
WaveFill, a wavelet-based inpainting network that decomposes images into
multiple frequency bands and fills the missing regions in each frequency band
separately and explicitly. WaveFill decomposes images by using discrete wavelet
transform (DWT) that preserves spatial information naturally. It applies L1
reconstruction loss to the decomposed low-frequency bands and adversarial loss
to high-frequency bands, hence effectively mitigate inter-frequency conflicts
while completing images in spatial domain. To address the inpainting
inconsistency in different frequency bands and fuse features with distinct
statistics, we design a novel normalization scheme that aligns and fuses the
multi-frequency features effectively. Extensive experiments over multiple
datasets show that WaveFill achieves superior image inpainting qualitatively
and quantitatively.
