Super-Resolution Reconstruction of Deep Residual Network with Multi-Level Skip Connections

The Fast Super-Resolution Convolutional Neural Network algorithm (FSRCNN) is difficult to extract deep image information due to the small number of convolution layers and the correlation lack between the feature information of adjacent convolutional layers. To solve this problem, a deep residual network super-resolution reconstruction method with multi-level skip connections is proposed. Firstly, a residual block with multi-level skip connections is designed to solve the problem that the characteristic information of adjacent convolutional layers lacks relevance. A deep residual network with multi-level skip connections is constructed on the basis of the residual block. Then, the deep residual network connected to the multi-level skip is trained by using the adaptive gradient rate strategy of Stochastic Gradient Descent (SGD) method and the network super-resolution reconstruction model is obtained. Finally, the low-resolution image is input into the deep residual network super-resolution reconstruction model with the multi-level skip connections, and the residual eigenvalue is obtained by the residual block connected the multi-level skip connections. The residual eigenvalue and the low resolution image are combined and converted into a high resolution image. The proposed method is compared with the bicubic, A+, SRCNN, FSRCNN and ESPCN algorithms in the Set5 and Set14 test sets. The proposed method is superior to other comparison algorithms in terms of visual effects and evaluation index values.