Related papers: Fundus image enhancement through direct diffusion bridges

Fundus image enhancement through direct diffusion bridges

URL: http://arxiv.org/abs/2409.12377v1
Date: Thu, 19 Sep 2024 00:26:14 GMT
Title: Fundus image enhancement through direct diffusion bridges
Authors: Sehui Kim, Hyungjin Chung, Se Hie Park, Eui-Sang Chung, Kayoung Yi, Jong Chul Ye,
Abstract summary: We propose FD3, a fundus image enhancement method based on direct diffusion bridges. We first propose a synthetic forward model through a human feedback loop with board-certified ophthalmologists. We train a robust and flexible diffusion-based image enhancement network that is highly effective as a stand-alone method.
Score: 44.31666331817371
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
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 \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

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