@article{Li_Zhou_Qi_Lu_Lu_2022, title={Best-Buddy GANs for Highly Detailed Image Super-resolution}, volume={36}, url={https://ojs.aaai.org/index.php/AAAI/article/view/20030}, DOI={10.1609/aaai.v36i2.20030}, abstractNote={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 ill-posed 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 rigid one-to-one constraint, we allow the estimated patches to dynamically seek trustworthy surrogates of 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.}, number={2}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Li, Wenbo and Zhou, Kun and Qi, Lu and Lu, Liying and Lu, Jiangbo}, year={2022}, month={Jun.}, pages={1412-1420} }