Abstract: The application of the deep network model has achieved great success in image super-resolution,and it has been proven that the reconstruction quality of low-resolution images reconstructed into high-resolution images is gene-rally higher than traditional algorithms.In order to further improve the reconstruction quality of image,a global residual recursive network was proposed .By optimizing the classical residual network,the global residual block feature fusion and the local residual block feature fusion are proposed,which allows the model to generate the idea of adaptive updating weights,and it improves information flow.In combination with the L1 cost function,the ADAM optimizer further improves training stability and trains the model through the DIV2K training set.Through the PSNR/SSIM image reconstruction index,the quality of picture reconstruction is obtained.In the SSIM index,the maximum value is 0.94,which is superior to 0.92 of the current latest deep learning model(EDSR).The global residual recursive network model effectively improves the image reconstruction quality,reduces straining time,effectively avoids gradient attenuation,and improves learning efficiency.

Key words: ADAM optimizer, DIV2K trai-ning set, Global residual recursive network, Image super-resolution, L1 cost function