ArticleBiomedical optics express2023
ESDiff: a joint model for low-quality retinal image enhancement and vessel segmentation using a diffusion model.
Article in Biomedical optics express, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- XVertNet: Unsupervised Contrast Enhancement of Vertebral Structures with Dynamic Self-Tuning Guidance and Multi-Stage Analysis.Journal of imaging informatics in medicine · 2026Article
- Beyond convolutions and supervised learning with transformers and representation learning for retinal image analysis.Progress in retinal and eye research · 2026Review
- Robust PCA withJournal of imaging · 2024Article
- CUNSB-RFIE: Context-aware Unpaired Neural Schrödinger Bridge in Retinal Fundus Image Enhancement.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer VisionArticle
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Authors and funding
2 authors.
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Abstract
In clinical screening, accurate diagnosis of various diseases relies on the extraction of blood vessels from fundus images. However, clinical fundus images often suffer from uneven illumination, blur, and artifacts caused by equipment or environmental factors. In this paper, we propose a unified framework called ESDiff to address these challenges by integrating retinal image enhancement and vessel segmentation. Specifically, we introduce a novel diffusion model-based framework for image enhancement, incorporating mask refinement as an auxiliary task via a vessel mask-aware diffusion model. Furthermore, we utilize low-quality retinal fundus images and their corresponding illumination maps as inputs to the modified UNet to obtain degradation factors that effectively preserve pathological features and pertinent information. This approach enhances the intermediate results within the iterative process of the diffusion model. Extensive experiments on publicly available fundus retinal datasets (i.e. DRIVE, STARE, CHASE_DB1 and EyeQ) demonstrate the effectiveness of ESDiff compared to state-of-the-art methods.
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Registered trials
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