VAOT: Vessel-Aware Optimal Transport for Retinal Fundus Enhancement
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arXiv
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| Format: | Preprint |
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2025
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| author | Dong, Xuanzhao Zhu, Wenhui Xiong, Yujian Chen, Xiwen Wang, Hao Li, Xin Cheng, Jiajun Wang, Zhipeng Tang, Shao Dumitrascu, Oana Wang, Yalin |
| author_facet | Dong, Xuanzhao Zhu, Wenhui Xiong, Yujian Chen, Xiwen Wang, Hao Li, Xin Cheng, Jiajun Wang, Zhipeng Tang, Shao Dumitrascu, Oana Wang, Yalin |
| contents | Color fundus photography (CFP) is central to diagnosing and monitoring retinal disease, yet its acquisition variability (e.g., illumination changes) often degrades image quality, which motivates robust enhancement methods. Unpaired enhancement pipelines are typically GAN-based, however, they can distort clinically critical vasculature, altering vessel topology and endpoint integrity. Motivated by these structural alterations, we propose Vessel-Aware Optimal Transport (\textbf{VAOT}), a framework that combines an optimal-transport objective with two structure-preserving regularizers: (i) a skeleton-based loss to maintain global vascular connectivity and (ii) an endpoint-aware loss to stabilize local termini. These constraints guide learning in the unpaired setting, reducing noise while preserving vessel structure. Experimental results on synthetic degradation benchmark and downstream evaluations in vessel and lesion segmentation demonstrate the superiority of the proposed methods against several state-of-the art baselines. The code is available at https://github.com/Retinal-Research/VAOT |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18763 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VAOT: Vessel-Aware Optimal Transport for Retinal Fundus Enhancement Dong, Xuanzhao Zhu, Wenhui Xiong, Yujian Chen, Xiwen Wang, Hao Li, Xin Cheng, Jiajun Wang, Zhipeng Tang, Shao Dumitrascu, Oana Wang, Yalin Computer Vision and Pattern Recognition Color fundus photography (CFP) is central to diagnosing and monitoring retinal disease, yet its acquisition variability (e.g., illumination changes) often degrades image quality, which motivates robust enhancement methods. Unpaired enhancement pipelines are typically GAN-based, however, they can distort clinically critical vasculature, altering vessel topology and endpoint integrity. Motivated by these structural alterations, we propose Vessel-Aware Optimal Transport (\textbf{VAOT}), a framework that combines an optimal-transport objective with two structure-preserving regularizers: (i) a skeleton-based loss to maintain global vascular connectivity and (ii) an endpoint-aware loss to stabilize local termini. These constraints guide learning in the unpaired setting, reducing noise while preserving vessel structure. Experimental results on synthetic degradation benchmark and downstream evaluations in vessel and lesion segmentation demonstrate the superiority of the proposed methods against several state-of-the art baselines. The code is available at https://github.com/Retinal-Research/VAOT |
| title | VAOT: Vessel-Aware Optimal Transport for Retinal Fundus Enhancement |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.18763 |