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The quality of a fundus image can be compromised by numerous factors, many ofwhich are challenging to be appropriately and mathematically modeled. In thispaper, we introduce a novel diffusion model based framework, named LearningEnhancement from Degradation (LED), for enhancing fundus images. Specifically,we first adopt a data-driven degradation framework to learn degradationmappings from unpaired high-quality to low-quality images. We then apply aconditional diffusion model to learn the inverse enhancement process in apaired manner. The proposed LED is able to output enhancement results thatmaintain clinically important features with better clarity. Moreover, in theinference phase, LED can be easily and effectively integrated with any existingfundus image enhancement framework. We evaluate the proposed LED on severaldownstream tasks with respect to various clinically-relevant metrics,successfully demonstrating its superiority over existing state-of-the-artmethods both quantitatively and qualitatively. The source code is available athttps://github.com/QtacierP/LED.

Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement