In this study, we propose a dual discriminator super-resolution reconstruction network (DDSRRN) that can improve the super-resolution reconstruction quality of images. By adding a discriminator based on generative adversarial networks, the DDSRRN combines the Kullback-Leibler (KL) divergence and reverse KL divergence into a unified objective function for training two discriminators. Thus, the complementary statistical properties obtained from these divergences can be exploited to effectively diversify the pre-estimated density under multiple modes. Additionally, model collapse is effectively avoided during the reconstruction process, and the model training stability is improved. The model loss function can be designed based on the Charbonnier loss function to estimate the content loss. Furthermore, the intermediate features of the network are used to design the perceptual loss and style loss. Finally, a deconvolution layer is designed to reconstruct the super-resolution images, thereby reducing the image reconstruction time. The proposed method is experimentally demonstrated to provide abundant details. Thus, the proposed method exhibits good generalization ability and obtains improved subjective visual evaluation and objective quantitative evaluation.