Recent studies have shown that the method combining the back-projection mechanism and deep convolutional neural network can well solve the problem of interdependence between high- and low-resolution images in single image super-resolution (SISR). However, this method still suffers from problems, such as large number of parameters and blurred detail textures. To address these problems, we propose a progressive residual feedback network with additional constraints for SISR. Specifically, we design a progressive residual structure that improves the densely connected network. This structure not only ensures feature reuse between each layer of the dense network through residual connections but also reduces the network complexity through progressive up-sampling reconstruction. In addition to enhance the detailed texture of the reconstructed image, we construct a spatial feature projection block based on the coordinate attention mechanism, so the extracted features contain spatial information that is relevant to the image reconstruction. Finally, we simulate the image degradation process with a degradation block to provide additional degradation constraints for the up-sampling network and optimize the convergence state-of-the-network. Numerous experimental results have proven the effectiveness of our method, which have advantages in both recovery quality and network complexity compared with other state-of-the-art SISR methods.