Fundus Image Enhancement

Fundus image enhancement aims to improve the quality of retinal photographs, crucial for accurate diagnosis and automated analysis of eye diseases. Current research focuses on developing deep learning models, including generative adversarial networks and diffusion models, often leveraging techniques like optimal transport and self-supervised learning to address challenges such as limited paired training data and the preservation of clinically relevant retinal structures. These advancements enable more reliable diagnoses and improve the efficiency of ophthalmological examinations, ultimately impacting both clinical practice and the development of computer-aided diagnostic tools.