To solve the problems of deep network gradient disappearance and image information loss caused by superimposition of simplex convolution layer when extracting image information in current image super-resolution reconstruction algorithms, an image super-resolution reconstruction algorithm based on multi-scale and residual network is proposed.The proposed algorithm uses multi-scale dense connection convolution kernel instead of the simplex accumulated convolution kernel, to fully extract the low-resolution image input information and realize reuse of channel feature dimension.The residual network is used to supplement the lost image information at multiple levels and suppress the gradient problem of the deep network model, which helps the whole network model to adaptively update the weights in the process of reverse propagation.In the end, the final reconstructed image is output through nonlinear mapping.Experiments show that:1) The peak signal-to-noise ratio and structural similarity of the proposed algorithm on the test set are improved compared with that of the contrast algorithms; and 2) In comparison with the current mainstream algorithms, the proposed algorithm obtains a reconstructed image with richer detail information and clearer edge texture.