Abstract: Because the traditional single image super-resolution reconstruction algorithm fails to make full use of the shallow feature information, ignores the spatial structure information in the visual target, is difficult to capture the dependence between the feature channel and the high-frequency feature information, and there are artifacts and edge blur in the reconstructed image, an image super-resolution reconstruction algorithm based on residual network and attention mechanism is proposed. The feature extraction part of the model combines the WDSR-B (Wider Activation Super-Resolution B) residual network to enhance the flow of feature information in the network, weights the feature parameters through the coordinate attention mechanism, and guides the network to better reconstruct high-frequency features and restore image details. The experimental results show that under quadruple image reconstruction, the PSNR(Peak Signal to Noise Ratio) on Set5 and Set14 test sets is 31. 00 dB and 28. 96 dB, and the SSIM( Structural Similarity) is 0. 893 and 0. 854. The reconstructed image performs better in detail and contour, which is better than other mainstream super resolution reconstruction algorithms.

Key words: residual network; , super resolution; , attention; , deep learning; , imageprocessing