A Generic Fundus Image Enhancement Network Boosted by Frequency
  Self-supervised Representation Learning
Heng Li Haofeng Liu Huazhu Fu Yanwu Xu Hui Shu Ke Niu Yan Hu Jiang Liu
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.