Abstract: Presently, in the field of single-frame image super-resolution (SISR), some deep networks are used to improve the image reconstruction effect through some intermediate features, such as simple cascading, channel attention, spatial attention, etc. in the reconstruction stage. However, people usually only pay attention to one of the directions. In this paper, the spatial channel attention based on SFT, and a progressive network are proposed, which based on the reconstruction of the spatial channel attention mechanism of spatial feature transform(SFT). The network uses intermediate features for image reconstruction from multiple angles. Firstly, it provides more similarity features based on SFT during feature extraction, and then it uses SFT spatial channel attention module (SFTCA module) to provide channel contribution strength and spatial dependence for image reconstruction. The experimental results show that, compared with most super-resolution algorithms, the proposed method has greatly improved the evaluation indexes during image super-resolution reconstruction, and the texture information of the reconstructed image is also clearer.