The trade-off between spatial and angular resolutions is one of the reasons for low-resolution light field images. Light field super-resolution techniques aim to reconstruct high-resolution light field images from low-resolution light field images. Deep learning-based light field super-resolution methods improve the quality of images by learning the mapping relationship between high- and low-resolution light field images. This advantage breaks through the limitations of traditional methods with high computational cost and complex operation. This paper provides a comprehensive overview of the research progress of deep learning-based light field super-resolution technology in recent years. The network framework and typical algorithms are examined, and experimental comparative analysis is conducted. Furthermore, the challenges faced in the area of light field super-resolution are summarized, and the future development direction is anticipated.