  The state-of-the-art facial image inpainting methods achieved promising
results but face realism preservation remains a challenge. This is due to
limitations such as; failures in preserving edges and blurry artefacts. To
overcome these limitations, we propose a Symmetric Skip Connection Wasserstein
Generative Adversarial Network (S-WGAN) for high-resolution facial image
inpainting. The architecture is an encoder-decoder with convolutional blocks,
linked by skip connections. The encoder is a feature extractor that captures
data abstractions of an input image to learn an end-to-end mapping from an
input (binary masked image) to the ground-truth. The decoder uses learned
abstractions to reconstruct the image. With skip connections, S-WGAN transfers
image details to the decoder. Additionally, we propose a Wasserstein-Perceptual
loss function to preserve colour and maintain realism on a reconstructed image.
We evaluate our method and the state-of-the-art methods on CelebA-HQ dataset.
Our results show S-WGAN produces sharper and more realistic images when
visually compared with other methods. The quantitative measures show our
proposed S-WGAN achieves the best Structure Similarity Index Measure (SSIM) of
0.94.
