多级跳线连接的深度残差网络超分辨率重建

由于快速的卷积神经网络超分辨率重建算法(FSRCNN)卷积层数少、相邻卷积层的特征信息之间缺乏关联性，因此难以提取到图像深层信息导致图像超分辨率重建效果不佳。针对此问题，该文提出多级跳线连接的深度残差网络超分辨率重建方法。首先，该方法设计了多级跳线连接的残差块，在多级跳线连接的残差块基础上构造了多级跳线连接的深度残差网络，解决相邻卷积层的特性信息缺乏关联性的问题；然后，使用随机梯度下降法(SGD)以可调节的学习率策略对多级跳线连接的深度残差网络进行训练，得到该网络超分辨率重建模型；最后，将低分辨率图像输入到多级跳线连接的深度残差网络超分辨率重建模型中，通过多级跳线连接的残差块得到预测的残差特征值，再将残差图像和低分辨率图像组合在一起转化为高分辨率图像。该文方法与bicubic, A+, SRCNN, FSRCNN和ESPCN算法在Set5和Set14测试集上进行了对比测试，在视觉效果和评价指标数值上该方法都优于其它对比算法。

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.