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.
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