ID 原文 译文
19475 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。 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.
19476 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。 To solve this problem, a deep residual network super-resolution reconstruction method with multi-level skip connections is proposed.
19477 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； 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.
19478 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； Then, the deep residual network connected to the multi-level skip is trainedby using the adaptive gradient rate strategy of Stochastic Gradient Descent (SGD) method and the networksuper-resolution reconstruction model is obtained.
19479 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。 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 eigen value 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.
19480 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。 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.
19481 为解决多信源多中继低密度奇偶校验(LDPC)码编码协作系统编码复杂度高、编码时延长的问题，该文引入一种特殊结构的LDPC码—基于生成矩阵的准循环LDPC码(QC-LDPC)码。该类码结合了QC-LDPC码与基于生成矩阵LDPC (G-LDPC)码的特点，可直接实现完全并行编码，极大地降低了中继节点的编码时延及编码复杂度。 To solve the problems of high encoding complexity and long encoding delay in the multi-source multi-relay Low Density Parity Check (LDPC) coded cooperative system, a special kind of structured LDPC codes—Quasi-Cyclic LDPC (QC-LDPC) codes based on generator matrix is proposed, which combines the characteristics of QC-LDPC codes and Generator-matrix-based LDPC (G-LDPC) codes. It can perform completely parallel encoding, which greatly reduces the encoding complexity and delay at the relays.
19482 在此基础上，推导出对应于信源节点和中继节点采用的QC-LDPC码的联合校验矩阵，并基于最大公约数(GCD)定理联合设计该矩阵以消除其所有围长为4, 6(girth-4, girth-6)的短环。 Based on this, a joint parity check matrix corresponding to the QC-LDPC codes adopted by the sources and relays is deduced, and the matrix is further jointly designed based on the Greatest Common Divisor (GCD) theorem to eliminate all cycles of girth-4 and girth-6.
19483 理论分析和仿真结果表明，在同等条件下该系统的误码率(BER)性能优于相应的点对点系统。 Theoretical analysis and simulation results show that under the same conditions, the Bit Error Rate (BER) performance of the proposed system is better than that of the corresponding point-to-point system.
19484 仿真结果还表明，与采用显式算法构造QC-LDPC码或一般构造QC-LDPC码的协作系统相比，采用联合设计QC-LDPC码的系统均可获得更高的编码增益。 The simulation results also show that the cooperative system with jointlydesigned QC-LDPC codes can obtain a higher coding gain than the system with explicitly constructedQC-LDPC codes or generally constructed QC-LDPC codes.