DBU-Net: Dual-Branch U-Net for Retinal Fundus Image Super-Resolution Under Complex Degradation Conditions

DBU-Net: Dual-Branch U-Net for Retinal Fundus Image Super-Resolution Under Complex Degradation Conditions

Retinal fundus images are widely utilized in clinical screening and diagnosis of ocular diseases. However, in practical scenarios, the obtained fundus images are prone to be low-resolution (LR). LR fundus images increase the uncertainty in clinical observations, thereby heightening the risk of misdi...

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Bibliographic Details
Main Authors: Xianghui Chen, Shi Qiu, Yue Wang, Yu Zhang, Zhaoyan Liu, Xinhong Wang, Weiyuan Yao, Hongjia Cheng, Feihong Wang, Zhan Shu, Xuesong Li
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Retinal fundus image
super-resolution
deep learning
U-Net
attention
multi-scale
Online Access:https://ieeexplore.ieee.org/document/10813356/
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https://ieeexplore.ieee.org/document/10813356/

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