The quality of a fundus image can be compromised by numerous factors, many of which are challenging to be appropriately and mathematically modeled. In this paper, we introduce a novel diffusion model based framework, named Learning Enhancement from Degradation (LED), for enhancing fundus images. Specifically, we first adopt a data-driven degradation framework to learn degradation mappings from unpaired high-quality to low-quality images. We then apply a conditional diffusion model to learn the inverse enhancement process in a paired manner. The proposed LED is able to output enhancement results that maintain clinically important features with better clarity. Moreover, in the inference phase, LED can be easily and effectively integrated with any existing fundus image enhancement framework. We evaluate the proposed LED on several downstream tasks with respect to various clinically-relevant metrics, successfully demonstrating its superiority over existing state-of-the-art methods both quantitatively and qualitatively. The source code is available at https://github.com/QtacierP/LED.

翻译：眼底图像的质量可能受到多种因素的影响，其中许多因素难以进行恰当且数学化的建模。本文提出一种基于扩散模型的新框架，命名为"从退化中学习增强"(LED)，用于眼底图像增强。具体而言，我们首先采用数据驱动的退化框架，学习从无配对的高质量图像到低质量图像的退化映射；随后应用条件扩散模型，以配对方式学习逆向增强过程。所提出的LED能够输出保留临床重要特征且具有更高清晰度的增强结果。此外，在推理阶段，LED可轻松有效地与任何现有眼底图像增强框架集成。我们在多个下游任务中，针对各种临床相关指标评估了所提出的LED，定量和定性结果均有力证明了其相较于现有最优方法的优越性。源代码已开源至 https://github.com/QtacierP/LED。