  We consider the single image super-resolution (SISR) problem, where a
high-resolution (HR) image is generated based on a low-resolution (LR) input.
Recently, generative adversarial networks (GANs) become popular to hallucinate
details. Most methods along this line rely on a predefined single-LR-single-HR
mapping, which is not flexible enough for the SISR task. Also, GAN-generated
fake details may often undermine the realism of the whole image. We address
these issues by proposing best-buddy GANs (Beby-GAN) for rich-detail SISR.
Relaxing the immutable one-to-one constraint, we allow the estimated patches to
dynamically seek the best supervision during training, which is beneficial to
producing more reasonable details. Besides, we propose a region-aware
adversarial learning strategy that directs our model to focus on generating
details for textured areas adaptively. Extensive experiments justify the
effectiveness of our method. An ultra-high-resolution 4K dataset is also
constructed to facilitate future super-resolution research.
