Structure-consistent Restoration Network for Cataract Fundus Image Enhancement

• URL: http://arxiv.org/abs/2206.04684v1
• Date: Thu, 9 Jun 2022 02:32:33 GMT
• Title: Structure-consistent Restoration Network for Cataract Fundus Image Enhancement
• Authors: Heng Li, Haofeng Liu, Huazhu Fu, Hai Shu, Yitian Zhao, Xiaoling Luo, Yan Hu, Jiang Liu
• Abstract summary: Fundus photography is a routine examination in clinics to diagnose and monitor ocular diseases. For cataract patients, the fundus image always suffers quality degradation caused by the clouding lens. To improve the certainty in clinical diagnosis, restoration algorithms have been proposed to enhance the quality of fundus images.
• Score: 33.000927682799016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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