摘要： 卷积神经网络（CNN）在单幅图像超分辨率重构中存在网络结构较浅、可提取特征较少和细节重构效果不显著等问题。为此，提出一种基于多通道极深CNN的图像超分辨率算法，分别对原始低分辨率图像进行3种插值和3种锐化等预处理操作，并以多通道图像作为CNN的输入层数据。通过重新调整卷积核大小以加深网络结构，使得输入层数据在极深的CNN模型中训练重构高分辨率图像。实验结果表明，与Bicubic、SRCNN和MC-SRCNN等算法相比，该算法的峰值信噪比和视觉效果均较好。

关键词: 卷积神经网络, 超分辨率重构, 多通道图像, 卷积核, 极深网络

Abstract: When applied in the super-resolution reconstruction of a single image,the structure of Convolutional Neural Network(CNN) is shallow,and the number of features that can be extracted is reduced,which weakens the reconstruction performance of details.To address the problem,this paper proposes a super-resolution reconstruction algorithm for images based on extremely deep CNN using multi-channel input.Three interpolation and three sharpening preprocessing operations are performed on the original low-resolution image respectively,and the multi-channel image is used as data of the input layer of CNN.At the same time,the size of the convolution kernel is readjusted to deepen the network structure,so that the data of the input layer is trained in an extremely deep CNN model to reconstruct high-resolution images.Experimental results show that compared with Bicubic,SRCNN,MC-SRCNN and other algorithms,this algorithm has a better Peak Signal-to-Noise Ratio(PSNR) and visual effects.

Key words: Convolutional Neural Network(CNN), super-resolution reconstruction, multi-channel image, convolution kernel, very deep network

中图分类号: 

• TP391