Symmetric Skip Connection Wasserstein GAN for High-resolution Facial Image Inpainting

Jireh Jam, Connah Kendrick, Vincent Drouard, Kevin Walker, Gee-Sern Hsu, Moi Yap

Abstract

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.

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Paper Citation


in Harvard Style

Jam J., Kendrick C., Drouard V., Walker K., Hsu G. and Yap M. (2021). Symmetric Skip Connection Wasserstein GAN for High-resolution Facial Image Inpainting.In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-488-6, pages 35-44. DOI: 10.5220/0010188700350044


in Bibtex Style

@conference{visapp21,
author={Jireh Jam and Connah Kendrick and Vincent Drouard and Kevin Walker and Gee-Sern Hsu and Moi Yap},
title={Symmetric Skip Connection Wasserstein GAN for High-resolution Facial Image Inpainting},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2021},
pages={35-44},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010188700350044},
isbn={978-989-758-488-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Symmetric Skip Connection Wasserstein GAN for High-resolution Facial Image Inpainting
SN - 978-989-758-488-6
AU - Jam J.
AU - Kendrick C.
AU - Drouard V.
AU - Walker K.
AU - Hsu G.
AU - Yap M.
PY - 2021
SP - 35
EP - 44
DO - 10.5220/0010188700350044