In order to obtain better image super-resolution reconstruction quality and improve the stability of network training, the generation of confrontation networks and loss functions are studied. Firstly, SRGAN and DenseNet are introduced, a generation network is designed to generate image based on DenseNet, and the sub-pixel convolution module is added to DenseNet. Then, the redundant BN layer in the original DenseNet is removed to improve the training efficiency of the model. Finally, the loss function of SRGAN is introduced and the loss function is redesigned based on the Earth-Mover distance, and the SmoothL1 loss is used to replace the MSE loss to calculate the VGG feature map to prevent MSE from amplifying the gap between the maximum error and the minimum error. Experiments prove that the model can achieve a stable convergence state during the network training process. The quality of the reconstructed image is compared with SRGAN,the average PSNR on the three benchmark test sets SET5, SET14, and BSD100 is about 2.02 dB higher, and SSIM is about 0042 (5.6%) higher. The reconstructed image not only has improved indicators, but also has better definition and richer high-frequency details.