Image super-resolution (SR) reconstruction is an important image processing technology to improve the resolution of image and video in computer vision. Image reconstruction model based on deep learning has not been satisfactory due to the too many layers involved, the excessively long training time resulting from difficult gradient transmission, and the unsatisfactory reconstructed image. This paper proposes a generative adversarial networks (GAN) image SR reconstruction model with improved residual block and adversarial loss. Firstly, on the model structure, the residual blocks of the excess batch normalization were designed and combined into a generative model, and the deep convolution network was used as the discriminant model to control the training direction of the reconstructed image to reduce the model’s calculation amount. Then, in the loss function, the Earth-Mover distance was designed to alleviate model gradient disappearance. The L1 distance was used as the measure of the degree of similarity between the reconstructed image and the high-resolution image to guide the model weight update to improve the reconstructed visual effect. Experimental results from the DIV2K, Set5, and Set14 datasets demonstrate that compared with the model before improvement, the training time of the proposed model was reduced by about 14% and the image reconstruction effect was effectively improved. For the loss function combined with Earth-Mover distance and L1 distance, gradient disappearance was effectively alleviated. Therefore, the proposed model significantly improved the SR reconstruction efficiency and visual effect of low-resolution images compared with Bicubic, SRCNN, VDSR, and DSRN model.