RFormer: Transformer-based Generative Adversarial Network for Real Fundus Image Restoration on A New Clinical Benchmark. (arXiv:2201.00466v1 [eess.IV])

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 …

arxiv generative adversarial network transformer