Retinal fundus photography is indispensable for ophthalmic screening and diagnosis, yet image quality is often degraded by noise, artifacts, and uneven illumination. Recent GAN- and diffusion-based enhancement methods improve perceptual quality by aligning degraded images with high-quality distributions, but our analysis shows that this focus can distort intra-class geometry: clinically related samples become dispersed, disease-class boundaries blur, and downstream tasks such as grading or lesion detection are harmed. The Gromov Wasserstein (GW) discrepancy offers a principled solution by aligning distributions through internal pairwise distances, naturally preserving intra-class structure, but its high computational cost restricts practical use. To overcome this, we propose SGW-GAN, the first framework to incorporate Sliced GW (SGW) into retinal image enhancement. SGW approximates GW via random projections, retaining relational fidelity while greatly reducing cost. Experiments on public datasets show that SGW-GAN produces visually compelling enhancements, achieves superior diabetic retinopathy grading, and reports the lowest GW discrepancy across disease labels, demonstrating both efficiency and clinical fidelity for unpaired medical image enhancement.

翻译：眼底摄影在眼科筛查与诊断中不可或缺，但图像质量常因噪声、伪影及光照不均而下降。近期基于生成对抗网络（GAN）和扩散模型的增强方法通过将退化图像与高质量分布对齐来提升感知质量，但我们的分析表明，这种聚焦可能扭曲类内几何结构：临床相关样本变得分散，疾病类别边界模糊，进而损害分级或病灶检测等下游任务。Gromov-Wasserstein（GW）差异通过内部成对距离对齐分布，天然保持类内结构，为此提供了原理性解决方案，但其高昂计算成本限制了实际应用。为克服此问题，我们提出SGW-GAN——首个将切片GW（SGW）融入眼底图像增强的框架。SGW通过随机投影近似GW，在显著降低成本的同时保持关系保真度。在公开数据集上的实验表明，SGW-GAN能生成视觉表现优异的增强图像，实现更优的糖尿病视网膜病变分级，并在疾病标签间报告最低的GW差异值，证明了其在非配对医学图像增强任务中的高效性与临床保真度。