  The purpose of image inpainting is to recover scratches and damaged areas
using context information from remaining parts. In recent years, thanks to the
resurgence of convolutional neural networks (CNNs), image inpainting task has
made great breakthroughs. However, most of the work consider insufficient types
of mask, and their performance will drop dramatically when encountering unseen
masks. To combat these challenges, we propose a simple yet general method to
solve this problem based on the LaMa image inpainting framework, dubbed GLaMa.
Our proposed GLaMa can better capture different types of missing information by
using more types of masks. By incorporating more degraded images in the
training phase, we can expect to enhance the robustness of the model with
respect to various masks. In order to yield more reasonable results, we further
introduce a frequency-based loss in addition to the traditional spatial
reconstruction loss and adversarial loss. In particular, we introduce an
effective reconstruction loss both in the spatial and frequency domain to
reduce the chessboard effect and ripples in the reconstructed image. Extensive
experiments demonstrate that our method can boost the performance over the
original LaMa method for each type of mask on FFHQ, ImageNet, Places2 and
WikiArt dataset. The proposed GLaMa was ranked first in terms of PSNR, LPIPS
and SSIM in the NTIRE 2022 Image Inpainting Challenge Track 1 Unsupervised.
