Spectral imaging using compressive sensing-based single-pixel modality

被引:1
作者
Majumder, S. [1 ,2 ]
Gupta, S. [2 ]
Dubey, S. [1 ]
机构
[1] Indian Inst Technol Delhi, SeNSE, New Delhi 110016, India
[2] Laser Sci & Technol Ctr, Metcalfe House, New Delhi 110054, India
关键词
medical image processing; data compression; image resolution; image sampling; image reconstruction; nondestructive testing; image sensors; cameras; compressed sensing; biomedical optical imaging; least squares approximations; conventional spectroscopic imaging techniques; massive acquisition time; spectral image; huge acquisition time; compressive sensing-based single-pixel camera architecture; spectro-spatial images; improved image quality; compressive sensing-based single-pixel modality; spectral imaging technique; CAMERA; TIME;
D O I
10.1049/el.2020.0757
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Spectral imaging technique plays a very vital role in the field of chemical detection and identification. Conventional spectroscopic imaging techniques suffer from massive acquisition time. This limitation sometimes restricts it from many practical applications. The acquisition of a full spectral image requires huge acquisition time. In this Letter, a compressive sensing-based single-pixel camera architecture has been realised to acquire spectral images that can be used for non-destructive testing and classification of explosive materials. The compressive measurements for all the spectral images are done simultaneously thus reducing the acquisition time significantly. The spectro-spatial images were reconstructed using the basis pursuit algorithm and compared with least square solutions, which resulted in fast acquisition and improved image quality. The maximum compression rate achieved was 95.84%.
引用
收藏
页码:1013 / 1015
页数:3
相关论文
共 24 条
[1]  
Abolghasemi V, 2010, EUR SIGNAL PR CONF, P427
[2]   Hyperspectral scanning white light interferometry based on compressive imaging [J].
Azari, Mohammad ;
Habibi, Nasim ;
Abolbashari, Mehrdad ;
Farahi, Faramarz .
EMERGING DIGITAL MICROMIRROR DEVICE BASED SYSTEMS AND APPLICATIONS VIII, 2016, 9761
[3]  
Bagheri S., 2013, Int. J. Sci. Eng. Res, V4, P253
[4]   Multispectral imaging using a single bucket detector [J].
Bian, Liheng ;
Suo, Jinli ;
Situ, Guohai ;
Li, Ziwei ;
Fan, Jingtao ;
Chen, Feng ;
Dai, Qionghai .
SCIENTIFIC REPORTS, 2016, 6
[5]   Multivariate Hyperspectral Raman Imaging Using Compressive Detection [J].
Davis, Brandon M. ;
Hemphill, Amanda J. ;
Maltas, Derya Cebeci ;
Zipper, Michael A. ;
Wang, Ping ;
Ben-Amotz, Dor .
ANALYTICAL CHEMISTRY, 2011, 83 (13) :5086-5092
[6]  
Galvis-Carreño Diana Fernanda, 2014, Dyna rev.fac.nac.minas, V81, P116
[7]   Spectral imaging with a single pixel camera [J].
Hayasaki, Yoshio ;
Sato, Ryo .
OPTICS AND PHOTONICS FOR INFORMATION PROCESSING XII, 2018, 10751
[8]   Reducing acquisition time in clinical MRI by data undersampling and compressed sensing reconstruction [J].
Hollingsworth, Kieren Grant .
PHYSICS IN MEDICINE AND BIOLOGY, 2015, 60 (21) :R297-R322
[9]  
Huri M A.M., 2017, Malaysian J. Anal. Sci, V21, P267, DOI [10.17576/mjas-2017-2102-01, DOI 10.17576/MJAS-2017-2102-01]
[10]   Hyperspectral imaging using the single-pixel Fourier transform technique [J].
Jin, Senlin ;
Hui, Wangwei ;
Wang, Yunlong ;
Huang, Kaicheng ;
Shi, Qiushuai ;
Ying, Cuifeng ;
Liu, Dongqi ;
Ye, Qing ;
Zhou, Wenyuan ;
Tian, Jianguo .
SCIENTIFIC REPORTS, 2017, 7