Focused on the problems of Single Image Super-Resolution (SISR) reconstruction methods, such as the loss of high frequency information during the process of image reconstruction, the introduction of noise during the process of upsampling and the difficulty of determining the interdependence relationships between the channels of the feature map, a deep progressive back-projection attention network was proposed. Firstly, a progressive upsampling method was used to gradually scale the Low Resolution (LR) image to a given magnification in order to alleviate problems such as high-frequency information loss caused by upsampling. Then, at each stage of progressive upsampling, iterative back-projection idea was merged to learn mapping relationship between High Resolution (HR) and LR feature maps and reduce the introduced noise in the upsampling process. Finally, the attention mechanism was used to dynamically allocate attention resources to the feature maps generated at different stages of the progressive back-projection network, so that the interdependence relationships between the feature maps were learned by the network model. Experimental results show that the proposed method can increase the Peak Signal-to-Noise Ratio (PSNR) by up to 3.16 dB and the structural similarity by up to 0.218 4.