Low Quality Fundus Image Enhancement Based on GAN for Automatic Glaucoma Detection

Handheld ophthalmoscopes often produce lowquality fundus images, which negatively affect the performance of deep learning models in diagnosing fundus diseases. Existing methods for fundus image enhancement mainly focus on visual improvement rather than optimizing the enhancement outcomes for specific diagnostic tasks. We propose a method for processing low-quality fundus images and diagnosing fundus diseases using an enhancement model. To address the lack of paired low/highquality glaucoma images, we artificially degraded images from the BrG public dataset. We then trained the RFormer enhancement model using transfer learning to transfer knowledge from images captured by traditional medical equipment to those captured by portable devices. The proposed method is designed with downstream diagnostic tasks in mind, creating a feedback loop that uses diagnostic model loss to guide realtime updates to the enhancement model. This approach not only boosts diagnostic performance but also enhances the perceived quality of the output images.