A generic fundus image enhancement network boosted by frequency self-supervised representation learning

Li，Heng1; Liu，Haofeng1; Fu，Huazhu2; Xu，Yanwu3,4; Shu，Hai5; Niu，Ke6; Hu，Yan1; Liu，Jiang1,7,8

2023-12-01

10.1016/j.media.2023.102945

Medical Image Analysis   Impact Factor & Quartile

1361-8415

1361-8423

Fundus photography is prone to suffer from image quality degradation that impacts clinical examination performed by ophthalmologists or intelligent systems. Though enhancement algorithms have been developed to promote fundus observation on degraded images, high data demands and limited applicability hinder their clinical deployment. To circumvent this bottleneck, a generic fundus image enhancement network (GFE-Net) is developed in this study to robustly correct unknown fundus images without supervised or extra data. Levering image frequency information, self-supervised representation learning is conducted to learn robust structure-aware representations from degraded images. Then with a seamless architecture that couples representation learning and image enhancement, GFE-Net can accurately correct fundus images and meanwhile preserve retinal structures. Comprehensive experiments are implemented to demonstrate the effectiveness and advantages of GFE-Net. Compared with state-of-the-art algorithms, GFE-Net achieves superior performance in data dependency, enhancement performance, deployment efficiency, and scale generalizability. Follow-up fundus image analysis is also facilitated by GFE-Net, whose modules are respectively verified to be effective for image enhancement.

Fundus image enhancement Seamless coupling Self-supervised representation learning Structure-aware representations

SCI

English

First ; Corresponding

National Natural Science Foundation of China[82102189];

Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging

Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging

WOS:001079214300001

ELSEVIER

COMPUTER SCIENCE

2-s2.0-85171165903

Scopus

1.Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China
2.Institute of High Performance Computing (IHPC),Agency for Science,Technology and Research (A*STAR),Singapore
3.School of Future Technology,South China University of Technology,Guangzhou,China
4.Pazhou Lab,Guangzhou,China
5.Department of Biostatistics,School of Global Public Health,New York University,United States
6.Computer School,Beijing Information Science and Technology University,Beijing,China
7.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China
8.Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Southern University of Science and Technology,Shenzhen,China

Li，Heng,Liu，Haofeng,Fu，Huazhu,et al. A generic fundus image enhancement network boosted by frequency self-supervised representation learning[J]. Medical Image Analysis,2023,90.

Li，Heng.,Liu，Haofeng.,Fu，Huazhu.,Xu，Yanwu.,Shu，Hai.,...&Liu，Jiang.(2023).A generic fundus image enhancement network boosted by frequency self-supervised representation learning.Medical Image Analysis,90.

Li，Heng,et al."A generic fundus image enhancement network boosted by frequency self-supervised representation learning".Medical Image Analysis 90(2023).