In order to solve the problem of large amounts of computation of super-resolution technology of deep layer convolution model

and fusion features are not comprehensive

a model structure is designed no longer deepened in depth

but expanded in width.Multi-size convolution processing is performed on an input feature map

residual structure

compression module and improved channel attention module are fused structurally

and the high-frequency and low-frequency information are flexibly used while fusing the multi-size feature map

and finally achieve the effect of improving the quality of the reconstructed image.The experimental results show that

compared with the currently popular super-resolution algorithm

the amount of parameters are reduced to a certain extent

and it has good performance in peak signal-to-noise ratio and structural similarity.