In order to improve the accuracy and efficiency of super-resolution reconstruction of lowresolution images, a multi-scale and multi-stage self similar fusion image super-resolution reconstruction algorithm is proposed: firstly, the low-frequency features of the image are obtained by using the feature extraction network and used as the input of two sub networks, one of which obtains the structural feature information of low-resolution images through the coding network, Secondly, the high-frequency features are obtained through the multi-path feedforward network composed of stage feature fusion unit, in which the fusion unit fuses the features of several consecutive layers of the network and obtains effective features in an adaptive way; Finally, the residual block and the batch normalization layer unfavorable to the image super-resolution in the discriminator are removed. Spectral normalization is used in the generator and discriminator to reduce the computational overhead and stabilize the model training. The experimental results show that Our method makes the average PSNR of the reconstructed images on the two test data sets of Remo-A dataset and Remo-B dataset reach 27.95 dB and the average SSIM reach 0.771 without losing too much speed. This model draws lessons from the connection mode of dense network to strengthen the connection between network layers and connect the whole network through multipath connection, so as to make full use of the characteristics of hierarchical network, extract more high-frequency information and improve the quality of reconstruction. The texture of the reconstructed results is more real, the brightness is more accurate and more in line with the evaluation of human visual senses, which shows the effectiveness and superiority of the algorithm. It not only performs faster in reconstruction speed, but also improves the quality of reconstructed image effectively.
