A Generic Fundus Image Enhancement Network Boosted by Frequency Self-supervised Representation Learning
This addresses image quality issues in fundus photography for ophthalmologists and AI systems, offering a more deployable solution with reduced data needs.
The paper tackles the problem of fundus image quality degradation by developing a generic enhancement network (GFE-Net) that corrects unknown images without supervised data, achieving superior performance in data dependency, enhancement, and generalizability compared to state-of-the-art methods.
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.