In recent years, with the rapid development of science and technology and the rise of deep learning, achieving image super-resolution reconstruction has become a hot research topic in the field of computer vision. However, the increase in network depth is easy to cause training difficulties, and the network cannot obtain accurate high-frequency information, resulting in poor image reconstruction. This study proposes an image super-resolution algorithm based on residual dense attention network to solve these problems. The algorithm mainly uses residual dense network, which accelerates the model convergence speed and reduces the gradient vanishing problem. The addition of attention mechanism makes the high-frequency effective information of the network have a larger weight and reduces the model calculation cost. Experiments show that the image super-resolution algorithm based on residual dense attention network greatly improves the model convergence speed, and the image detail recovery effect is satisfactory.