双通道多感知卷积神经网络图像超分辨率重建

基于深度卷积神经网络的单幅图像超分辨率重建取得了显著研究成果.但随着深度卷积神经网络规模的不断扩大，如何降低网络构建难度和计算成本成为一个难点.为此，提出了一种双通道多感知卷积神经网络(DMCN)模型.该模型在两条具有不同卷积核的通道上建立了稠密连接，并构建了带有动态调节能力的层间融合结构.这种结构的设计使得小规模卷积神经网络便能获得图片特征信息的全面感知能力.实验结果表明，DMCN重建效果优于目前多数具有代表性的重建算法.

Dual-channel Multi-perception Convolutional Network for Image Super-Resolution

Rececnt researches on single-image super-resolution based on deep convolutional neural networks have achieved remarkable results. However， with increasing neural network scale， to reduce the network constructing difficulty and the computation costs has become a difficult issue. To solve this problem， a dual-channel multi-perception convolutional neural network (DMCN) is proposed. Specifically， the dense skip connection between two convolutional channels with different sizes of convolution kernels is established. Then， an adjustable inter-layer fusion structure is established between different layers. This structure makes it possible to learn abundant image feature information through a small-scale convolutional neural network. The experimental results show that the proposed model outperforms most of the typical algorithms.