Retinal fundus photography is significant in diagnosing and monitoring retinal diseases. However, systemic imperfections and operator/patient-related factors can hinder the acquisition of high-quality retinal images. Previous efforts in retinal image enhancement primarily relied on GANs, which are limited by the trade-off between training stability and output diversity. In contrast, the Schr\"{o}dinger Bridge (SB), offers a more stable solution by utilizing Optimal Transport (OT) theory to model a stochastic differential equation (SDE) between two arbitrary distributions. This allows SB to effectively transform low-quality retinal images into their high-quality counterparts. In this work, we leverage the SB framework to propose an image-to-image translation pipeline for retinal image enhancement. Additionally, previous methods often fail to capture fine structural details, such as blood vessels. To address this, we enhance our pipeline by introducing Dynamic Snake Convolution, whose tortuous receptive field can better preserve tubular structures. We name the resulting retinal fundus image enhancement framework the Context-aware Unpaired Neural Schr\"{o}dinger Bridge (CUNSB-RFIE). To the best of our knowledge, this is the first endeavor to use the SB approach for retinal image enhancement. Experimental results on a large-scale dataset demonstrate the advantage of the proposed method compared to several state-of-the-art supervised and unsupervised methods in terms of image quality and performance on downstream tasks.The code is available at \url{https://github.com/Retinal-Research/CUNSB-RFIE}.

本研究解决了视网膜眼底图像质量不高的问题，尤其是在获取过程中受到系统性缺陷和操作/患者因素的影响。提出了一种基于薛定谔桥框架的图像到图像翻译管道，通过引入动态蛇卷积以更好地保留细微结构，如血管，实现了视网膜图像增强。实验结果表明，该方法在图像质量和下游任务性能上优于现有的多种方法。

上下文感知无配对神经薛定谔桥在视网膜眼底图像增强中的应用