High-Quality Face Image SR Using Conditional Generative Adversarial Networks

Huang Bin; Chen Weihai; Wu Xingming; Lin Chun-Liang

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arXiv · 1707.00737

High-Quality Face Image SR Using Conditional Generative Adversarial Networks

Huang Bin·Chen Weihai·Wu Xingming·Lin Chun-Liang

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Abstract

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.

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BibTeX RIS

Huang Bin, Chen Weihai, Wu Xingming, Lin Chun-Liang. 2017-07-04. High-Quality Face Image SR Using Conditional Generative Adversarial Networks. https://arxiv.org/abs/1707.00737

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