License plate image reconstruction plays an important role in the intelligent transportation system. After repeated experiments, a super-resolution image reconstruction method for license plates is proposed with the help of generative adversarial networks (GANs). The method mainly consists of four parts: (1) pretreatment of the input image, including image resizing and filtering of images with poor contrast; (2) image feature extraction using a residual dense network; (3) introduction of progressive sampling, which can provide a larger receptive field and more information details; (4) introduction of a discriminator based on PatchGAN to make a more accurate judgment, which guides the generator to reconstruct images with higher quality and more details. The comparison with a current superior algorithm on the Chinese City Parking Dataset (CCPD) proves that the proposed model has higher PSNR and SSIM (26.80 and 0.77, respectively) and less time of reconstructing a single-frame image (only 0.06 s), which verifies the feasibility of the proposed approach in license plate image reconstruction.