摘要
基于压缩感知和单像素成像的基本原理,设计了一种用于图像超分辨率重建的新型深度卷积神经网络架构。这种单像素超分辨率成像算法成功地将深度学习图像超分辨率重建技术与压缩感知单像素成像技术相结合,从而发展出一种全新的深度学习单像素成像优化方法。与传统的常规压缩感知图像重构算法相比,该算法有效提升了图像超分辨率重建精度和单像素成像质量。通过图像重建的仿真实验和单像素相机的成像实验验证,结果表明这种基于深度学习的新型单像素相机成像方式具有良好的性能表现。
Based on the basic principles of compressed sensing(CS)and single pixel camera imaging,a new deep convolutional neural network architecture for image super-resolution reconstruction is redesigned.The new single pixel super-resolution imaging algorithm successfully combines deep learning image super-resolution reconstruction technology with compressed sensing single pixel imaging technology to develop an entirely new deep learning based optimized imaging method of single pixel camera.Compared with the conventional CSbased algorithms,the accuracy of image super-resolution reconstruction and the imaging quality of single pixel camera are improved effectively.The good performance of the new proposed deep learning imaging method of single pixel camera has been verified by simulation experiments of image reconstruction and imaging experiments of single pixel camera.
作者
魏子然
杨威
张建林
徐智勇
刘永
王盛杰
WEI Ziran;YANG Wei;ZHANG Jianlin;XU Zhiyong;LIU Yong;WANG Shengjie(Institute of Optics and Electronics of the Chinese Academy of Sciences,Chengdu 610209,CHN;School of Optoelectronic Science and Engineering,University of Electronic Science and Technology of China,Chengdu 610054,CHN;University of Chinese Academy of Sciences,Beijing 100049,CHN;Civil Aviation Flight University of China,Guanghan 618307,CHN)
出处
《半导体光电》
北大核心
2021年第3期412-417,共6页
Semiconductor Optoelectronics
关键词
单像素成像
压缩感知
深度学习
超分辨率
图像重构
single pixel imaging
compressed sensing
deep learning
super-resolution
image reconstruction
