摘要
为降低无人机硬件设备升级成本,研究利用深度学习技术进行航拍图像超分辨(super-resolution,SR)。针对神经网络训练参数量大的特点,提出了一种稀疏卷积神经网络SR(SR based on sparse convolutional neural network,SRSCNN)重构方法,对神经网络连接权值进行选择性筛选达到压缩网络结构并减少训练时间的目的。实验结果表明,该方法在缩短网络学习时间,图像重构效果和计算时间上具有一定优越性。同时,设计了一种基于显著性区域的图像质量评价方式,更适应航拍图像后续处理工作。
In order to reduce the cost of unmanned aerial vehicle(UAV)hardware upgrades,super-resolution(SR)based on deep learning of aerial images is studied.SR based on sparse convolutional neural network(SRSCNN)is proposed to compress network structure and reduce the training time by selectively screening the weights of the neural network connections.The experimental results show that the method can effectively shorten the network learning time required under conditions of superiority of the reconstruction effect and computing time.Meanwhile,a saliency-map-based image quality assessment method is designed,which is more suitable for the follow-up processing of aerial image.
作者
王彩云
李阳雨
李晓飞
王佳宁
魏文怡
WANG Caiyun;LI Yangyu;LI Xiaofei;WANG Jianing;WEI Wenyi(College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;Beijing Institute of Electronic System Engineering, Beijing 100854, China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2021年第8期2045-2050,共6页
Systems Engineering and Electronics
基金
国家自然科学基金(61301211)
国家留学基金(201906835017)资助课题。
关键词
图像超分辨
深度学习
卷积神经网络
航拍图像
图像质量评价
image super-resolution(SR)
deep learning
convolutional neural network
aerial image
image quality assessment
