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- W4287905146 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." @default.
- W4287905146 created "2022-07-26" @default.
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- W4287905146 date "2020-01-11" @default.
- W4287905146 modified "2023-09-23" @default.
- W4287905146 title "Symmetric Skip Connection Wasserstein GAN for High-Resolution Facial Image Inpainting" @default.
- W4287905146 doi "https://doi.org/10.48550/arxiv.2001.03725" @default.
- W4287905146 hasPublicationYear "2020" @default.
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