High-Quality Face Image SR Using Conditional Generative Adversarial Networks
arXiv:1707.00737
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.
9 pages, 4 figures
References in corpus (8)
- Conditional Generative Adversarial Nets
- Densely Connected Convolutional Networks
- Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
- Energy-based Generative Adversarial Network
- BEGAN: Boundary Equilibrium Generative Adversarial Networks
- Towards Principled Methods for Training Generative Adversarial Networks
- The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
- Deep Joint Face Hallucination and Recognition