Non-mydriatic retinal color fundus photography (CFP) is widely available due to the advantage of not requiring pupillary dilation, however, is prone to poor quality due to operators, systemic imperfections, or patient-related causes. Optimal retinal image quality is mandated for accurate medical diagnoses and automated analyses. Herein, we leveraged the Optimal Transport (OT) theory to propose an unpaired image-to-image translation scheme for mapping low-quality retinal CFPs to high-quality counterparts. Furthermore, to improve the flexibility, robustness, and applicability of our image enhancement pipeline in the clinical practice, we generalized a state-of-the-art model-based image reconstruction method, regularization by denoising, by plugging in priors learned by our OT-guided image-to-image translation network. We named it as regularization by enhancing (RE). We validated the integrated framework, OTRE, on three publicly available retinal image datasets by assessing the quality after enhancement and their performance on various downstream tasks, including diabetic retinopathy grading, vessel segmentation, and diabetic lesion segmentation. The experimental results demonstrated the superiority of our proposed framework over some state-of-the-art unsupervised competitors and a state-of-the-art supervised method.

翻译：非散瞳视网膜彩色眼底照相（CFP）因无需散瞳而广泛可用，但由于操作者、系统缺陷或患者相关原因，容易导致图像质量不佳。准确的医学诊断和自动化分析要求视网膜图像具有最佳质量。在此，我们利用最优传输（OT）理论提出了一种非配对图像到图像翻译方案，用于将低质量视网膜CFP映射为高质量图像。此外，为提高图像增强流程在临床实践中的灵活性、鲁棒性和适用性，我们通过注入由OT引导的图像到图像翻译网络学习的先验知识，推广了一种最先进的基于模型的图像重建方法——去噪正则化，并将其命名为增强正则化（RE）。我们在三个公开的视网膜图像数据集上验证了集成框架OTRE，评估了增强后的质量及其在多种下游任务（包括糖尿病视网膜病变分级、血管分割和糖尿病病灶分割）中的性能。实验结果表明，我们提出的框架优于一些最先进的无监督竞争对手以及一种最先进的有监督方法。