OTRE：最优传输引导的无配对图像到图像转换与通过增强进行正则化相结合
OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing
AI总结：
本文提出OTRE框架，结合最优传输理论的无配对图像转换与通过增强进行正则化（RE）方法，将低质量视网膜眼底图像映射为高质量图像，并在多个数据集和下游任务上验证了其优于现有无监督和有监督方法的性能。
摘要：
非散瞳视网膜彩色眼底摄影（CFP）因无需散瞳的优势而被广泛使用，然而由于操作人员、系统缺陷或患者相关原因，其容易出现图像质量不佳的问题。准确的医学诊断和自动化分析需要最优的视网膜图像质量。在此，我们利用最优传输（OT）理论，提出了一种无配对图像到图像转换方案，用于将低质量的视网膜CFP映射为高质量图像。此外，为了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，我们通过引入由OT引导的图像到图像转换网络学习的先验，推广了一种最先进的基于模型的图像重建方法——通过去噪进行正则化，并将其命名为通过增强进行正则化（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，评估了增强后的图像质量及其在各种下游任务上的表现，包括糖尿病视网膜病变分级、血管分割和糖尿病病变分割。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方法和一种最先进的有监督方法。
英文摘要：
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.
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