@inproceedings{a1e01acd3a214c00aff5e2da1dfab986,
title = "AnimeTransGAN: Animation Image Super-Resolution Transformer via Deep Generative Adversarial Network",
abstract = "To achieve better visual experiences for watching classic animations displayed on high-end displays, such as UHDTV (ultra high-definition television), a novel deep learning framework designed for animation image super-resolution (SR) is proposed in this paper. To overcome the possible drawbacks that GAN (generative adversarial network)-based SR models may not recover sufficient image details while transformer-based SR models may produce over-blurred/over-sharpened images, we propose a novel GAN-based model for animation image SR, where we integrate the superior detail recovery capability of transformer models for image SR, and the discriminative ability of the U-Net-based discriminator for determining the reality of generated images, denoted by AnimeTransGAN. Our experimental results demonstrate that the proposed AnimeTransGAN model quantitatively and qualitatively achieves better SR performances for animation images, compared with the state-of-the-art methods.",
keywords = "animation image, deep learning, generative adversarial network, self-attention, super-resolution, transformer",
author = "Peng, {Chang De} and Kang, {Li Wei}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 12th IEEE Global Conference on Consumer Electronics, GCCE 2023 ; Conference date: 10-10-2023 Through 13-10-2023",
year = "2023",
doi = "10.1109/GCCE59613.2023.10315278",
language = "English",
series = "GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "250--251",
booktitle = "GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics",
}