摘要
针对VDCN网络结构在大尺度因子上超分辨率效果较差的缺点,提出一种高精度单图像超分辨率重建方法。将ReLU激活函数更换为PReLU激活函数,增加网络层数,使用25个带PReLU激活函数的卷积层进行训练和测试。实验结果表明,与VDCN方法相比,该方法耗费时间较少,且性能更稳定。
To solve the disadvantage of VDCN network structure in large scale factors,a new high-precision single image super-resolution method is proposed.The ReLU activation function is replaced with the PReLU activation function,and the number of network layers is increased.The model uses 25 convolution layers with PReLU activation functions to train and test.Experimental results show compared with the VDCN method,this method takes less time and has stable performance.
作者
连逸亚
吴小俊
LIAN Yiya;WU Xiaojun(College of Internet of Things Engineering,Jiangnan University,Wuxi,Jiangsu 214122,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2019年第1期217-220,共4页
Computer Engineering
基金
国家自然科学基金(61672265)
关键词
卷积神经网络
图像超分辨率
PReLU激活函数
深度学习
网络深度
Convolutional Neural Network(CNN)
image Super-Resolution(SR)
PReLU activation function
deep learning
network depth
