  Image inpainting is one of the important tasks in computer vision which
focuses on the reconstruction of missing regions in an image. The aim of this
paper is to introduce an image inpainting model based on Wasserstein Generative
Adversarial Imputation Network. The generator network of the model uses
building blocks of convolutional layers with different dilation rates, together
with skip connections that help the model reproduce fine details of the output.
This combination yields a universal imputation model that is able to handle
various scenarios of missingness with sufficient quality. To show this
experimentally, the model is simultaneously trained to deal with three
scenarios given by missing pixels at random, missing various smaller square
regions, and one missing square placed in the center of the image. It turns out
that our model achieves high-quality inpainting results on all scenarios.
Performance is evaluated using peak signal-to-noise ratio and structural
similarity index on two real-world benchmark datasets, CelebA faces and Paris
StreetView. The results of our model are compared to biharmonic imputation and
to some of the other state-of-the-art image inpainting methods.
