Fundus Image Enhancement Through Direct Diffusion Bridges

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Abstract
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 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 .
Funding Information
  • National Research Foundation of Korea (RS-2024-00336454, RS-2023-00262527)
  • Korea Medical Device Development Fund
  • Ministry of Science and ICT, South Korea
  • Ministry of Trade, Industry and Energy
  • Ministry of Health & Welfare
  • Ministry of Food and Drug Safety (1711137899, KMDF_PR_20200901_0015)
  • Ministry of Science and ICT, South Korea
  • Information Technology Research Center (IITP-2024-2020-0-01461)
  • Institute for Information & Communications Technology Planning & Evaluation
  • Institute of Information & Communications Technology Planning & Evaluation (RS-2021-II212068)
  • Artificial Intelligence Innovation Hub

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