An Annotation-free Restoration Network for Cataractous Fundus Images
This addresses the need for more practical restoration tools in clinics for cataract patients, but it is incremental as it builds on existing restoration methods by removing annotation requirements.
The paper tackled the problem of restoring cataractous fundus images without requiring annotations, which limits clinical application, and achieved favorable performance against state-of-the-art algorithms, promoting diagnosis of ocular fundus diseases.
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.