  Cataracts are the leading cause of vision loss worldwide. Restoration
algorithms are developed to improve the readability of cataract fundus images
in order to increase the certainty in diagnosis and treatment for cataract
patients. Unfortunately, the requirement of annotation limits the application
of these algorithms in clinics. This paper proposes a network to
annotation-freely restore cataractous fundus images (ArcNet) so as to boost the
clinical practicability of restoration. Annotations are unnecessary in ArcNet,
where the high-frequency component is extracted from fundus images to replace
segmentation in the preservation of retinal structures. The restoration model
is learned from the synthesized images and adapted to real cataract images.
Extensive experiments are implemented to verify the performance and
effectiveness of ArcNet. Favorable performance is achieved using ArcNet against
state-of-the-art algorithms, and the diagnosis of ocular fundus diseases in
cataract patients is promoted by ArcNet. The capability of properly restoring
cataractous images in the absence of annotated data promises the proposed
algorithm outstanding clinical practicability.
