用直接扩散桥提升眼底图像质量，支持多种退化类型
Fundus image enhancement through direct diffusion bridges
- 基于眼科医生反馈构建合成前向模型，优化图像退化模拟
- 在真实患者数据上显著改善白内障与小瞳孔患者的图像质量
- 首次实现独立使用的扩散模型增强，无需依赖预训练模型
我们提出FD3，一种基于直接扩散桥的眼底图像增强方法，可应对雾霾、模糊、噪声和阴影等多种复杂退化。通过与资深眼科医生协作的反馈循环，构建了用于最大化真实低质量图像质量提升的合成前向模型。利用该模型，训练出一个鲁棒且灵活的基于扩散的图像增强网络，作为独立方法表现出色，不同于以往需依赖预训练模型的扩散增强方法。大量实验表明，FD3不仅在合成退化数据上表现优异，在真实患者（如白内障或小瞳孔）拍摄的低质量眼底图像上也展现出优越性能。为推动该领域研究，我们开源全部代码与数据，地址为 https://github.com/heeheee888/FD3
原文摘要 · Abstract (English)
We propose FD3, a fundus image enhancement method based on direct diffusion bridges, which can cope with a wide range of complex degradations, including haze, blur, noise, and shadow. We first propose a synthetic forward model through a human feedback loop with board-certified ophthalmologists for maximal quality improvement of low-quality in-vivo images. Using the proposed forward model, we train a robust and flexible diffusion-based image enhancement network that is highly effective as a stand-alone method, unlike previous diffusion model-based approaches which act only as a refiner on top of pre-trained models. Through extensive experiments, we show that FD3 establishes \add{superior quality} not only on synthetic degradations but also on in vivo studies with low-quality fundus photos taken from patients with cataracts or small pupils. To promote further research in this area, we open-source all our code and data used for this research at https://github.com/heeheee888/FD3
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