中文摘要：

为提取更多有效特征并提高模型训练的收敛速度,提出一种基于并列卷积神经网络的超分辨率重建方法。该网络由两路不同结构的网络组成：一路为简单的残差网络,其优化残差映射比原始的映射更容易实现;另一路为增加了非线性映射的卷积神经网络,增强了网络的非线性能力。随着并行网络结构的复杂化,收敛速度慢成为突出问题。针对这个问题,在卷积层后添加正则化处理,以简化模型参数、增强特征拟合能力,最终达到加快收敛的目的。实验结果表明,与基于深度卷积神经网络算法相比,该网络结构收敛速度更快,主观视觉效果更好,峰值信噪比（PSNR）平均提高了0.2dB。

英文摘要：

To extract more effective features and speed up the convergence of model training, a super-resolution reconstruction algorithm based on parallel convolution neural network was proposed. The network consists of two different network structures, one is a simple residual network structure, which has a easier optimal residual mapping than the original one; the other is a convolutional neural network with nonlinear mapping, which can increase the non-linearity of the network.As the complexity of the parallel network structure, the convergence speed is the key issue. Aiming at this problem, the Local Response Normalization（LRN） layer was added to the convolution layers to simplify the model parameters and enhance the feature fitting ability, thus accelerating the convergence. Experimental results show that, compared with algorithms based on deep convolutional neural network, the proposed method accelerates the convergence, improves the visual quality, and increases Peak Signal-to-Noise Ratio（PSNR） at least 0. 2 dB.