  Fundus photography is a routine examination in clinics to diagnose and
monitor ocular diseases. However, for cataract patients, the fundus image
always suffers quality degradation caused by the clouding lens. The degradation
prevents reliable diagnosis by ophthalmologists or computer-aided systems. To
improve the certainty in clinical diagnosis, restoration algorithms have been
proposed to enhance the quality of fundus images. Unfortunately, challenges
remain in the deployment of these algorithms, such as collecting sufficient
training data and preserving retinal structures. In this paper, to circumvent
the strict deployment requirement, a structure-consistent restoration network
(SCR-Net) for cataract fundus images is developed from synthesized data that
shares an identical structure. A cataract simulation model is firstly designed
to collect synthesized cataract sets (SCS) formed by cataract fundus images
sharing identical structures. Then high-frequency components (HFCs) are
extracted from the SCS to constrain structure consistency such that the
structure preservation in SCR-Net is enforced. The experiments demonstrate the
effectiveness of SCR-Net in the comparison with state-of-the-art methods and
the follow-up clinical applications. The code is available at
https://github.com/liamheng/ArcNet-Medical-Image-Enhancement.
