<p>Fundus images are capable of accurately depicting the fundus structure of the examinee, providing crucial diagnostic evidence for systemic diseases like hypertension, enabling early detection, and facilitating timely preventive interventions. Nevertheless, fundus images often exhibit varying degrees of quality defects during acquisition. These imperfections not only undermine the accuracy of manual diagnosis but also pose significant challenges to automated analysis. Recently, deep learning-based image enhancement techniques have witnessed remarkable progress in addressing the issue of image quality defects. However, the direct application of these techniques to low-quality fundus images is still fraught with limitations. This article presents a comprehensive and systematic summary of deep learning techniques for fundus image enhancement, including a brief description and analysis of existing state-of-the-art approaches, datasets, and evaluation metrics. Comparisons between such methods are also illustrated. Finally, challenges and future research directions are discussed.</p>