Paper Info
Title
Symmetric Skip Connection Wasserstein Gan For High-Resolution Facial Image Inpainting
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.
Year
DOI
Venue
2021
10.5220/0010188700350044
VISAPP: PROCEEDINGS OF THE 16TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS - VOL. 4: VISAPP
Keywords
DocType
Citations 
Inpainting, Generative Neural Networks, Hallucinations, Realism
Conference
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Jam Jireh100.34
Kendrick Connah200.34
Drouard Vincent300.34
Walker Kevin400.34
Gee-Sern Hsu56214.59
Moi Hoon Yap619027.82