Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement
This work addresses the challenge of enhancing medical fundus images for clinical applications, representing an incremental improvement over existing methods.
The paper tackles the problem of enhancing low-quality fundus images by proposing a diffusion model framework that learns degradation mappings and inverse enhancement, resulting in improved clarity and clinical feature preservation, with demonstrated superiority over state-of-the-art methods in quantitative and qualitative evaluations.
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.