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）因无需瞳孔扩张而广泛应用，但易受操作者、系统缺陷或患者相关因素影响导致图像质量不佳。准确的医学诊断和自动化分析要求视网膜图像达到最佳质量。为此，本文利用最优传输（OT）理论提出一种无配对图像到图像翻译方案，将低质量视网膜CFP映射为高质量图像。为提升临床实践中图像增强流程的灵活性、鲁棒性和适用性，我们通过植入由OT引导的图像到图像翻译网络学习到的先验信息，对基于模型的最先进图像重建方法（去噪正则化）进行泛化，将其命名为增强正则化（RE）。我们在三个公开视网膜图像数据集上验证了集成框架OTRE的性能，通过评估增强后的图像质量及其在糖尿病视网膜病变分级、血管分割和糖尿病病变分割等下游任务中的表现，实验结果表明所提框架在性能上优于一些最先进的无监督竞争方法和一种有监督方法。