The single image super-resolution (SISR) reconstruction algorithm is ill-posed in the mapping learning from low-resolution (LR) image to high-resolution (HR) image, and the deep neural network has slow convergence and lacks the ability to learn high-frequency information. Moreover, image feature information tends to be lost during deep neural network propagation. In order to address these issues, this study proposes an image super-resolution reconstruction network based on dual regression and residual attention mechanism. Firstly, the mapping space is constrained by dual regression. Secondly, a residual attention module (RCSAB) is constructed by combining channel and spatial attention mechanisms, which not only accelerates the model convergence speed and effectively strengthens the learning of high-frequency information. Finally, a dense feature fusion module is introduced to enhance the fluidity of feature information. In addition, a comparison with the mainstream SISR algorithms is carried out on four benchmark datasets, namely, Set5, Set14, BSD100, and Urban100, and experimental results demonstrate that the proposed method is superior to other algorithms in terms of objective evaluation metrics and subjective visual effects.