SAT-Net: Structure-Aware Transformer-Based Attention Fusion Network for Low-Quality Retinal FunduImages Enhancement

In ophthalmology diagnosis, high-fidelity fundus images are essential for disease diagnosis and intervention. However, many real-world clinical conditions may degrade the quality of the acquired images and thus affect clinical diagnostic accuracy. Traditional convolutional neural network-based retinal fundus image enhancement methods cannot always capture long-range dependencies, which reduces the overall visual quality of images, especially for real retinal fundus images. Furthermore, existing enhancement methods often fail to fully utilize low-resolution structural detail information, which potentially leads to inaccurate pivotal fundus vessel topology or capillary details. In this paper, we propose a novel…

• NN
  National Natural Science Foundation of China

  Awards: 62101346, 62301330
• BA
  Basic and Applied Basic Research Foundation of Guangdong Province

  Awards: 2022A1515110101, 2024A1515010496