High-Quality Face Image SR Using Conditional Generative Adversarial Networks

By Huang Bin, Weihai Chen, Xingming Wu, Chun-Liang Lin. 2017

24 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

  1. Divya Saxena
  2. Jiannong Cao

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