Real-world non-mydriatic retinal fundus photography is prone to artifacts, imperfections and low-quality when certain ocular or systemic co-morbidities exist. Artifacts may result in inaccuracy or ambiguity in clinical diagnoses. In this paper, we proposed a simple but effective end-to-end framework for enhancing poor-quality retinal fundus images. Leveraging the optimal transport theory, we proposed an unpaired image-to-image translation scheme for transporting low-quality images to their high-quality counterparts. We theoretically proved that a Generative Adversarial Networks (GAN) model with a generator and discriminator is sufficient for this task. Furthermore, to mitigate the inconsistency of information between the low-quality images and their enhancements, an information consistency mechanism was proposed to maximally maintain structural consistency (optical discs, blood vessels, lesions) between the source and enhanced domains. Extensive experiments were conducted on the EyeQ dataset to demonstrate the superiority of our proposed method perceptually and quantitatively.

翻译：真实世界中的非散瞳视网膜眼底摄影在存在特定眼部或全身合并症时，易出现伪影、瑕疵及低质量情况。这些伪影可能导致临床诊断的准确性不足或模糊不清。本文提出了一种简洁且有效的端到端框架，用于增强低质量视网膜眼底图像。基于最优传输理论，我们设计了一种非配对图像到图像的翻译方案，将低质量图像转换为高质量对应图像。我们理论上证明了包含生成器和判别器的生成对抗网络模型足以完成此任务。此外，为缓解低质量图像与其增强结果之间的信息不一致性，我们提出了信息一致性机制，以最大程度保持源域与增强域之间的结构一致性（视盘、血管、病变）。在EyeQ数据集上进行了大量实验，从感知和定量角度证明了我们方法的优越性。