  Face super-resolution is a domain-specific image super-resolution, which aims
to generate High-Resolution (HR) face images from their Low-Resolution (LR)
counterparts. In this paper, we propose a novel face super-resolution method,
namely Semantic Encoder guided Generative Adversarial Face Ultra-Resolution
Network (SEGA-FURN) to ultra-resolve an unaligned tiny LR face image to its HR
counterpart with multiple ultra-upscaling factors (e.g., 4x and 8x). The
proposed network is composed of a novel semantic encoder that has the ability
to capture the embedded semantics to guide adversarial learning and a novel
generator that uses a hierarchical architecture named Residual in Internal
Dense Block (RIDB). Moreover, we propose a joint discriminator which
discriminates both image data and embedded semantics. The joint discriminator
learns the joint probability distribution of the image space and latent space.
We also use a Relativistic average Least Squares loss (RaLS) as the adversarial
loss to alleviate the gradient vanishing problem and enhance the stability of
the training procedure. Extensive experiments on large face datasets have
proved that the proposed method can achieve superior super-resolution results
and significantly outperform other state-of-the-art methods in both qualitative
and quantitative comparisons.
