We propose a novel single face image super-resolution method, which named Face Conditional Generative Adversarial Network(FCGAN), based on boundary equilibrium generative adversarial networks. Without taking any facial prior information, our method can generate a high-resolution face image from a low-resolution one. Compared with existing studies, both our training and testing phases are end-to-end pipeline with little pre/post-processing. To enhance the convergence speed and strengthen feature propagation, skip-layer connection is further employed in the generative and discriminative networks. Extensive experiments demonstrate that our model achieves competitive performance compared with state-of-the-art models.
@article{arxiv.1707.00737,
title = {High-Quality Face Image SR Using Conditional Generative Adversarial Networks},
author = {Huang Bin and Chen Weihai and Wu Xingming and Lin Chun-Liang},
journal= {arXiv preprint arXiv:1707.00737},
year = {2017}
}