Fundus image enhancement through direct diffusion bridges
This work addresses image quality issues in ophthalmology for improved diagnosis, but it is incremental as it builds on diffusion-based methods.
The authors tackled the problem of enhancing low-quality fundus images degraded by haze, blur, noise, and shadow, and achieved superior quality results on both synthetic and in vivo patient data, including from those with cataracts or small pupils.
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