ing at the loss of information and edge blurring during texture recovery using super-resolution technology based on convolution neural networks, we combine dense block and squeeze module to learn the mapping from low-resolution to high-resolution in an end-to-end manner. The dense block structure formed by the fusion of dense connection utilizes context information of image region effectively. The squeeze module amplifies valuable global information selectively and suppresses the useless features. The multiple 1×1 convolution layer structures in the image reconstruction section reduce the dimension of the previous layers, and speed up the calculation while reducing the loss of information. Processing the original image directly shortens the training time, and the optimization of convolution layers and filters reduces the computational complexity significantly.