  Retinal fundus images are widely used for the clinical screening and
diagnosis of eye diseases. However, fundus images captured by operators with
various levels of experience have a large variation in quality. Low-quality
fundus images increase uncertainty in clinical observation and lead to the risk
of misdiagnosis. However, due to the special optical beam of fundus imaging and
structure of the retina, natural image enhancement methods cannot be utilized
directly to address this. In this paper, we first analyze the ophthalmoscope
imaging system and simulate a reliable degradation of major inferior-quality
factors, including uneven illumination, image blurring, and artifacts. Then,
based on the degradation model, a clinically oriented fundus enhancement
network (cofe-Net) is proposed to suppress global degradation factors, while
simultaneously preserving anatomical retinal structures and pathological
characteristics for clinical observation and analysis. Experiments on both
synthetic and real images demonstrate that our algorithm effectively corrects
low-quality fundus images without losing retinal details. Moreover, we also
show that the fundus correction method can benefit medical image analysis
applications, e.g., retinal vessel segmentation and optic disc/cup detection.
