ing at the problems of blurred edge details and loss of image features in the process of image super-resolution reconstruction, a super-resolution reconstruction algorithm based on dense connection generative adversarial network is proposed. This algorithm consists of a generative network and a discriminative network. In the generative network structure, the original low-resolution image is used as the input of the network. In order to make full use of the features, the features of the shallow network are transferred to each layer of the deep network structure using dense connection, so as to effectively avoid the loss of image features. Sub-pixel convolution is performed at the end, and the image is deconvolved to complete the final super-resolution reconstruction of the image, which greatly reduces the training time. In the discriminative network structure, 6 convolutional modules and a fully connected layer are used to identify true and false images, and the idea of adversarial games is used to improve the quality of reconstructed images. Experimental results show that the proposed algorithm has greatly improved the visual effect assessment, peak signal to noise ratio value, structural similarity value, time-consuming, and indicators. It has restored richer image detail information and achieved better visual effects and comprehensive characteristic.