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

[41] Compressive Sensing for Sparse Touch Detection on Capacitive Touch Screens
Luo, Chenchi
Borkar, Milind A.
Redfern, Arthur J.
McClellan, James H.
IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS, 2012, 2 (03) : 639 - 648
[42] CS-LeCT: Chained Secure and Low-Energy Consumption Data Transmission Based on Compressive Sensing
Zhang, Jun
Zhou, Jiaxin
Gu, Zhenghui
Zhang, Zhi
Wang, Luhua
Yu, Zhu Liang
Li, Yuanqing
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2023, 72
[43] Secure compressive sensing of images based on combined chaotic DWT sparse basis and chaotic DCT measurement matrix
Wang, Zhongpeng
Hussein, Zakarie Said
Wang, Xiumin
OPTICS AND LASERS IN ENGINEERING, 2020, 134 (134)
[44] Fractal compressed sensing imaging with sparse difference based on fractal and entropy recognition
Liu, J. -X.
Sun, N.
Han, G.
Du, K.
Li, X. -F.
Sun, Q. -S.
IMAGING SCIENCE JOURNAL, 2015, 63 (04) : 203 - 213
[45] A Study on Dictionary Selection in Compressive Sensing for ECG Signals Compression and Classification
Fira, Monica
Costin, Hariton-Nicolae
Goras, Liviu
BIOSENSORS-BASEL, 2022, 12 (03):
[46] Deep Unfolding With Weighted l1 Minimization for Compressive Sensing
Zhang, Jun
Li, Yuanqing
Yu, Zhu Liang
Gu, Zhenghui
Cheng, Yu
Gong, Huoqing
IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (04) : 3027 - 3041
[47] Optimising compressive sensing matrix using Chicken Swarm Optimisation algorithm
Aziz, Ahmed
Singh, Karan
Osamy, Walid
Khedr, Ahmed M.
IET WIRELESS SENSOR SYSTEMS, 2019, 9 (05) : 306 - 312
[48] Modified compressive sensing approach for GNSS signal reception in the presence of interference
Chang, Chung-Liang
GPS SOLUTIONS, 2016, 20 (02) : 201 - 213
[49] Semiconductor superlattice physical unclonable function based two-dimensional compressive sensing cryptosystem and its application to image encryption
Suo, Zhufeng
Dong, Youheng
Tong, Fenghua
Jiang, Donghua
Fang, Xi
Chen, Xiaoming
INFORMATION SCIENCES, 2022, 618 : 227 - 252
[50] Image parallel block compressive sensing scheme using DFT measurement matrix
Wang, Zhongpeng
Jiang, Yannan
Chen, Shoufa
MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 82 (14) : 21561 - 21583

← 1 2 3 4 5 →