Structure-consistent Restoration Network for Cataract Fundus Image Enhancement

TL;DR

This paper introduces SCR-Net, a structure-preserving restoration network for cataract-affected fundus images, trained on synthesized data to improve clinical diagnosis without extensive real-world data collection.

Contribution

The paper proposes a novel structure-consistent restoration network trained on synthesized data, effectively preserving retinal structures in cataract fundus images.

Findings

SCR-Net outperforms state-of-the-art methods in image restoration quality.

The method enhances clinical diagnosis accuracy for cataract patients.

Effective structure preservation demonstrated in experiments.

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.