Generative Adversarial Networks for Retinal Image Enhancement with Pathological Information

Abstract

Age- related macular degeneration (AMD) is a disease of the central retina, which is one of the main reasons for vision loss of elderly people. To monitor the level of AMD, the doctors mainly use the retinal fundus images. However, the quality of retinal images can be affected during the imaging process. It leads to low contrast and blurry images. Those bad quality images can not be used for analyzing and diagnosis. For that reason, there are many studies about image enhancement in order to improve the quality of retinal photography. However, previous methods could not guarantee to keep the disease information after the enhancement process. Therefore, we introduce a generative adversarial model for AMD retinal image enhancement with additional factors to preserve the disease information. By exploiting drusen segmentation masks, our proposed model can enhance retinal photography quality and keep the pathological information.