Computational Spectral Imaging Based on Compressive Sensing

被引：8

作者：

Wang, Chao ^{[1
,2
,3
]}

Liu, Xue-Feng ^{[3
]}

Yu, Wen-Kai ^{[1
]}

Yao, Xu-Ri ^{[3
]}

Zheng, Fu ^{[3
]}

Dong, Qian ^{[3
]}

Lan, Ruo-Ming ^{[4
]}

Sun, Zhi-Bin ^{[3
]}

Zhai, Guang-Jie ^{[3
]}

Zhao, Qing ^{[1
]}

机构：

[1] Beijing Inst Technol, Sch Phys, Ctr Quantum Technol Res, Beijing 100081, Peoples R China

[2] China Acad Engn Phys, Mianyang 621900, Peoples R China

[3] Chinese Acad Sci, Key Lab Elect & Informat Technol Space Syst, Natl Space Sci Ctr, Beijing 100190, Peoples R China

[4] Shandong Normal Univ, Sch Phys & Elect, Jinan 250014, Shandong, Peoples R China

来源：

CHINESE PHYSICS LETTERS | 2017年 / 34卷 / 10期

基金：

中国国家自然科学基金;

关键词：

PROJECTIONS;

D O I：

10.1088/0256-307X/34/10/104203

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

Spectral imaging is an important tool for a wide variety of applications. We present a technique for spectral imaging using computational imaging pattern based on compressive sensing (CS). The spectral and spatial information is simultaneously obtained using a fiber spectrometer and the spatial light modulation without mechanical scanning. The method allows high-speed, stable, and sub sampling acquisition of spectral data from specimens. The relationship between sampling rate and image quality is discussed and two CS algorithms are compared.

引用

页数：5

共 50 条

[21] Super-resolution compressive spectral imaging via two-tone adaptive coding
Xu, Chang
Xu, Tingfa
Yan, Ge
Ma, Xu
Zhang, Yuhan
Wang, Xi
Zhao, Feng
Arce, Gonzalo R.
PHOTONICS RESEARCH, 2020, 8 (03) : 395 - 411
[22] Adaptive Compressive Sensing Based Direction of Arrival Estimation Using Particle Filters
Ergun, Reyhan
Kilic, Berkan
Kalfa, Mert
Arikan, Orhan
2020 28TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2020,
[23] Compressive Sensing of Object-Signature
Tamir, Dan E.
Shaked, Natan T.
Geerts, Wilhelmus J.
Dolev, Shlomi
OPTICAL SUPERCOMPUTING, 2011, 6748 : 63 - +
[24] Data Gathering with Compressive Sensing in Wireless Sensor Networks: A Random Walk Based Approach
Zheng, Haifeng
Yang, Feng
Tian, Xiaohua
Gan, Xiaoying
Wang, Xinbing
Xiao, Shilin
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2015, 26 (01) : 35 - 44
[25] Compressive Sensing-Based Power Allocation Optimization for Energy Harvesting IoT Nodes
Zhang, Jun
Xie, Guangfei
Han, Guojun
Yu, Zhu Liang
Gu, Zhenghui
Li, Yuanqing
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2022, 21 (06) : 4535 - 4548
[26] Fractal pursuit for compressive sensing signal recovery
Liu, Jixin
Sun, Quansen
COMPUTERS & ELECTRICAL ENGINEERING, 2013, 39 (02) : 420 - 432
[27] Compressive Rendering: A Rendering Application of Compressed Sensing
Sen, Pradeep
Darabi, Soheil
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2011, 17 (04) : 487 - 499
[28] An Improved Toeplitz Measurement Matrix for Compressive Sensing
Xu Su
Yin Hongpeng
Chai Yi
Xiong Yushu
Tan Xue
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, 2014,
[29] Fast AFM Imaging Based on Neural Network Compressed Sensing
Sun, Meng
Chen, Na
Li, Shaoying
Liu, Zhenmin
Ye, Shuai
Shang, Yana
Liu, Shupeng
Pang, Fufei
Wang, Tingyun
2022 IEEE 24TH INTERNATIONAL WORKSHOP ON MULTIMEDIA SIGNAL PROCESSING (MMSP), 2022,
[30] Random-Frequency SAR Imaging Based on Compressed Sensing
Yang, Jungang
Thompson, John
Huang, Xiaotao
Jin, Tian
Zhou, Zhimin
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2013, 51 (02): : 983 - 994

← 1 2 3 4 5 →