@inproceedings{
ding2024masked,
title={Masked Snake Attention for Fundus Image Restoration with Vessel Preservation},
author={Xiaohuan Ding and Gong Yangrui and Tianyi Shi and Zihang Huang and Gangwei Xu and Xin Yang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=bzSbhuoRfr}
}
Restoring low-quality fundus images, especially the recovery of vessel structures, is crucial for clinical observation and diagnosis. Existing state-of-the-art methods use standard convolution and window based self-attention block to recover low-quality fundus images, but these feature capturing approaches do not effectively match the slender and tortuous structure of retinal vessels. Therefore, these methods struggle to accurately restore vessel structures. To overcome this challenge, we propose a novel low-quality fundus image restoration method called Masked Snake Attention Network (MSANet). It is designed specifically for accurately restoring vessel structures. Specifically, we introduce the Snake Attention module (SA) to adaptively aggregate vessel features based on the morphological structure of the vessels. Due to the small proportion of vessel pixels in the image, we further present the Masked Snake Attention module (MSA) to more efficiently capture vessel features. MSA enhances vessel features by constraining snake attention within regions predicted by segmentation methods. Extensive experimental results demonstrate that our MSANet outperforms the state-of-the-art methods in enhancement evaluation and downstream segmentation tasks.
Masked Snake Attention for Fundus Image Restoration with Vessel Preservation
Xiaohuan Ding Gong Yangrui Tianyi Shi Zihang Huang Gangwei Xu Xin Yang