A fundus image enhancer based on illumination-guided attention and optic disc perception GAN

Abstract

High-quality fundus images are essential for ophthalmologists in clinical diagnosis of eye diseases. For reasons such as the acquisition process and the retinal disease, most fundus images may suffer poor illumination, blur, and low contrast. In order to obtain high-quality fundus images, a novel network model for fundus image enhancement is constructed in this paper. The model introduces a generated adversarial network (GAN) with illumination-guided attention and optic disc perception. A U-Net with illumination-guided attention is used as the generator, and a global discriminator and a local discriminator with optic disc segmentation are used in the learning process. The model can improve the exposure level of overexposed and underexposed fundus images and also stretch the contrast. Experiments are performed on multiple fundus image datasets taken by the handheld device and high-end device to verify the robustness of the proposed illumination-guided attention and optic disc perception GAN (IGAODPGAN). The retinal structure and pathological characteristics are highlighted significantly after enhancement. Our method is superior to other state-of-the-art algorithms in terms of both subjective and objective assessment, which can provide an efficient solution for fundus image enhancement, especially for the non-uniform illuminated fundus images.