To address the issue that large amount of data processing and poor performance of super-resolution reconstruction of license plate images in surveillance video, in this paper, a super-resolution construction algorithm of license plate image based on gradual back-projection network is proposed. First, in order to reduce the amount of super-resolution network data processing, the low-resolution license plate area is detected and extracted. Second, the larger sampling multiples of deep back-projection network (DBPN) are decomposed and the iterative back projection is completed by sampling step by step. In the gradual back-projection unit, skip connection fuses the middle-scale features produced by the gradual sampling operations to improve the feature utilization rate. 1×1 convolutional layer is used for reducing dimensions of fused middle-scale features, while preserving key information. Finally, the high-resolution license plate image is reconstructed according to the feature image generated by the gradual projection unit. Experimental results show that the proposed algorithm not only reduces the amount of data processing and parameters of the super-resolution network, but also greatly improves the subjective feeling and objective evaluation index of the reconstructed license plate image quality compared with DBPN.