A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement

Abstract

Eye fundus image quality represents a significant factor involved in ophthalmic screening. Usually, eye fundus image quality is affected by artefacts, brightness, and contrast hindering ophthalmic diagnosis. This paper presents a conditional generative adversarial network-based method to enhance eye fundus image quality, which is trained using automatically generated synthetic bad-quality/good-quality image pairs. The method was evaluated in a public eye fundus dataset with three classes: good, usable and bad quality according to specialist annotations with 0.64 Kappa. The proposed method enhanced the image quality from usable to good class in 72.33% of images. Likewise, the image quality was improved from the bad category to usable class, and from bad to good class in 56.21% and 29.49% respectively.