Fundus image enhancement through direct diffusion bridges
<|reference_start|>Fundus image enhancement through direct diffusion bridges: 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<|reference_end|>
@article{kim2024fundus,
    title={Fundus image enhancement through direct diffusion bridges},
    author={Sehui Kim, Hyungjin Chung, Se Hie Park, Eui-Sang Chung, Kayoung Yi,
  Jong Chul Ye},
    journal={arXiv preprint arXiv:2409.12377},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12377},
    primaryClass={eess.IV cs.CV}
}