基于卷积神经网络的轻量级图像超分辨率

最近几年，深层卷积神经网络在解决单图像超分辨率问题上有着不错的表现。为了改善卷积神经网络的层数越深带来的计算量越大和实时重建速度越慢的缺点,结合现有的卷积网络模型，本文提出一种轻量级的网络结构。在神经网络层中减少网络层数，利用通道分离构建出局部特征的多尺度增强结构，进一步地结合残差网络进行模型构建。实验结果表明，与LapSRN方法、VDSR方法、传统的插值法等相比，该方法实时重建速度较快，且在峰值信噪比和结构相似性上不弱于其他方法。

Lightweight Image Super-Resolution Based on Convolutional Neural Network

In recent years, deep convolutional neural networks have performed well in solving single image super-resolution problems. For improving the disadvantage that, the deeper the layer number of convolutional neural network is, the greater the amount of calculation is, the slower the real-time reconstruction speed is, combined with the existing convolutional network model, a lightweight network structure is proposed. This paper reduces the number of network layers in the neural network layer and uses channel split to build a multi-scale enhanced structure of local features. Then it combines the residual network for model construction. Experiment results show that, compared with LapSRN method, VDSR method, and traditional interpolation method, this method is faster in real-time reconstruction and is not weaker than others in peak signal to noise ratio and structural similarity.