  Ophthalmologists have used fundus images to screen and diagnose eye diseases.
However, different equipments and ophthalmologists pose large variations to the
quality of fundus images. Low-quality (LQ) degraded fundus images easily lead
to uncertainty in clinical screening and generally increase the risk of
misdiagnosis. Thus, real fundus image restoration is worth studying.
Unfortunately, real clinical benchmark has not been explored for this task so
far. In this paper, we investigate the real clinical fundus image restoration
problem. Firstly, We establish a clinical dataset, Real Fundus (RF), including
120 low- and high-quality (HQ) image pairs. Then we propose a novel
Transformer-based Generative Adversarial Network (RFormer) to restore the real
degradation of clinical fundus images. The key component in our network is the
Window-based Self-Attention Block (WSAB) which captures non-local
self-similarity and long-range dependencies. To produce more visually pleasant
results, a Transformer-based discriminator is introduced. Extensive experiments
on our clinical benchmark show that the proposed RFormer significantly
outperforms the state-of-the-art (SOTA) methods. In addition, experiments of
downstream tasks such as vessel segmentation and optic disc/cup detection
demonstrate that our proposed RFormer benefits clinical fundus image analysis
and applications. The dataset, code, and models are publicly available at
https://github.com/dengzhuo-AI/Real-Fundus
