Photoacoustic imaging （PAI） is a newly emerging hybrid functional imaging modality. High-quality image reconstruction is the key to improve the imaging accuracy. Incomplete photoacoustic（PA） measurements usually lead to the reduction in the imaging depth and the quality of images which are rendered by using conventional reconstruction techniques such as back projection （BP）， time reversal （TR）， and delay and sum （DAS）. The iterative algorithms are capable of solving this issue to a certain extent at the cost of high computational burden and a properly selected regularization tool. In recent years， deep learning （DL） has exhibited promising performances in the field of medical imaging. It has also shown great potential in reconstructing images with high quality and high efficiency. This paper provides a survey on PA image reconstruction from sparely sampled data in a limited view based on DL. The current methods are summarized and classified， and their advantages and limits are also discussed.