High-Quality Face Image SR Using Conditional Generative Adversarial Networks
This work addresses face image super-resolution for applications like surveillance or photography, but it is incremental as it builds on existing GAN frameworks with modifications like skip-layer connections.
The authors tackled the problem of single face image super-resolution by proposing a novel method called Face Conditional Generative Adversarial Network (FCGAN), which generates high-resolution images from low-resolution inputs without facial priors and achieves competitive performance compared to state-of-the-art models.
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.