Quantitative comparison on private datasets.
Version 1ProblemLow-quality fundus images with complex degredation can cause costly re-examinations of patients or inaccurate clinical diagnosis.AimThis study aims to create an automatic fundus macular image enhancement framework to improve low-quality fundus images and remove complex image degradation.MethodWe propose a new deep learning-based model that automatically enhances low-quality retinal fundus images that suffer from complex degradation. We collected a dataset, comprising 1068 pairs of high-qu
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ProblemLow-quality fundus images with complex degredation can cause costly re-examinations of patients or inaccurate clinical diagnosis.AimThis study aims to create an automatic fundus macular image enhancement framework to improve low-quality fundus images and remove complex image degradation.MethodWe propose a new deep learning-based model that automatically enhances low-quality retinal fundus images that suffer from complex degradation. We collected a dataset, comprising 1068 pairs of high-qu