Image super-resolution is widely used in medical and security fields.Aiming at the shortcomings that traditional super-resolution reconstruction (SR) methods cannot reconstruct edge feature images,this paper proposes a reconstruction scheme based on prior information and dense connected network model.By taking into account the different combinations of residual features of input statistical information,a multi attention module is introduced to improve the network performance without adding additional modules by cooperating with the backbone network structure.The proposed model has better performance than the existing state of the art (SOTA) model with complex structures. In order to avoid the sharp drift of the input identity features,a network module of attention mechanism based on prior information is proposed to estimate the real low resolution (LR) counterpart.This model has advantages in terms of capturing motion noise,etc.The experimental results show that this method has more advantages in evaluation indicators and subjective visual analysis than other mainstream methods.