For the intelligent driving field, it is necessary to obtain high-quality super-resolution images under the condition of limited memory. Therefore, a vehicle-based image super-resolution reconstruction algorithm based on weighted eight-bit binary quantization was proposed. Firstly, the information compression module was designed based on the eight-bit binary quantization convolution, reducing the internal redundancy, enhancing the information flow in the network, and improving the reconstruction rate. Then, the whole network was composed of a feature extraction module, a plurality of stacked information compression modules and an image reconstruction module, and the information of the interpolated super-resolution space was fused with the image reconstructed by the low-resolution space, improving the network expression ability without increasing the complexity of the model. Finally, the entire network structure in the algorithm was trained based on the Generative Adversarial Network (GAN) framework, making the image have better subjective visual effect. The experimental results show that, the Peak Signal-to-Noise Ratio (PSNR) of the proposed algorithm for the reconstructed vehicle-based image is 0.22 dB higher than that of Super-Resolution using GAN (SRGAN), its generated model size is reduced to 39% of that of the Laplacian pyramid Networks for fast and accurate Super-Resolution (LapSRN), and the reconstruction speed is improved to 7.57 times of that of LapSRN.