A super-resolution reconstruction method based on improved edge-attention generation adversarial network is proposed for low-resolution thermal imaging images of power equipment. First, using edge attention, a dual attention (DA) module of channel and position attention is introduced to capture the dependencies between different positions of the feature map and between different channels. The two sets of dependencies are fused to increase the degree of global information extraction. Then, to address the problem that the parametric rectified linear unit (PReLU) activation function performs undifferentiated activation on the neurons in the network, which leads to the limited feature expression capability of the network. The improved function is used to replace the PReLU function and selectively activate the neurons on the basis of identifying the effective features in order to strengthen effective features and weaken the ineffective features, and enhance the adaptive activation and feature expression capabilities of the network. Finally, the proposed improved edge-attention generative adversarial network (EA-GAN) model is experimentally validated. The results show that compared with BiCubic and the original EA-GAN model, the proposed improved model has the best network performance, the highest reconstructed image quality, and the best objective evaluation indices of peak signal-to- noise ratio (PSNR), structural similarity (SSIM) and mean square error loss (MSE-loss) mean values. These are universal in the field of infrared image reconstruction of power equipment and have a certain engineering application value.