%0 Journal Article %A LIU Nanyan %A MA Shengxiang %A Wei Hongfei %T Super-Resolution Reconstruction of Single-Frame Images in Cross-Scale Multi-Branching Networks %D 2022 %R 10.3778/j.issn.1002-8331.2203-0307 %J Computer Engineering and Applications %P 257-266 %V 58 %N 19 %X Super-resolution reconstruction algorithm is one of the key research problems in the field of computer vision. At present, the image requirements in various fields are gradually increasing, and the existing super-resolution reconstruction algorithms improve the image quality by deepening a single network depth, ignoring the computational complexity problem when reconstructing, and lacking the use of image own information to improve the image reconstruction effect. To address the above problems, a cross-scale multi-branching single-frame image supe-resolution reconstruction network is proposed. The cross-scale module explores the correlation of similar regions within the image and is used to enhance the ability to reconstruct image detail information and obtain better visual effects. The multi-branching structure reconstructs regions in the image with different reconstruction difficulty by networks of different complexity separately, solving the problem of high computational complexity of deep networks. Experimental results show that the model has higher peak signal-to-noise ratio and structural similarity on Urban100 and Manga109 test sets compared with other methods. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2203-0307