|
|
|
|
|
|
Reconstruction Simulation with Quantum Dots Spectral Imaging Technology |
WANG Ying-jun1, 2,ZHOU Jin-song1, 2, WEI Li-dong1*, ZHANG Gui-feng1, ZHU Dong-liang3, GUO San-wei3, TANG Hong-wu3, PANG Dai-wen3 |
1. Key Laboratory of Computational Optical Imaging Technology, Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Key Laboratory of Analytical Chemistry for Biology and Medicine (Ministry of Education), College of Chemistry and Molecular Sciences, The Institute for Advanced Studies, Wuhan University, Wuhan 430072, China |
|
|
Abstract In order to meet the requirement of compact type and lightweight for spectral imaging system in airborne and satellite platform, and to overcome the limitations of optical splitting system in current spectral imaging technology such as complex structure and high cost, for the first time we present the design of spectral imaging based on quantum dots. In this method, a strip of quantum dots array is placed in front of the focal plane of telescope lens and absorption properties of quantum dots materials is applied to modulate the incident spectrum of the target, then least square method is applied to establish the spectral reconstruction model of the target. Finally, the spectral and spatial information of the target is obtained with the method of push broom and spectral reconstruction. The quantum dots spectral imaging technology has the advantages of high spectral resolution, high energy availability, small size, wide spectral range and low cost. More important, the effects of different spectral intervals and noises on the reconstructed spectral resolution and their impact on the accuracy or distortion of the reconstructed spectra are analyzed. The results show that the spectral resolution increases with the decrease of the spectral interval, and the accuracy and resolution of the reconstructed spectrum are reduced with the increase of the noise level. What's more, the accuracy of reconstruction can be improved by appropriately increasing the spectral interval. With a comparison of the simulation results with the known data cube, the feasibility of the technology is verified, and the results show that the quantum dots spectral imaging possesses higher quality. In conclusion, quantum dots provide a new approach for spectral imaging technology, which has wide applications in the field of aerospace and other miniature spectral remote sensing.
|
Received: 2017-03-01
Accepted: 2017-07-30
|
|
Corresponding Authors:
WEI Li-dong
E-mail: weilidong@aoe.ac.cn
|
|
[1] Wolf W L. Introduction to Imaging Spectrometers. SPIE Optical Engineering Press, 1997. 1.
[2] CUI Ting-wei, MA Yi, ZHANG Jie(崔廷伟,马 毅,张 杰). Remote Sensing Technology and Application(遥感技术与应用), 2003, 18(2): 118.
[3] WANG Jian-yu, SHU Rong, LIU Yin-hua(王建宇,舒 嵘,刘银华). Introduction to Imaging Spectroscopy(成像光谱技术导论). Beijing: Science Press(北京:科学出版社),2011. 90.
[4] Umpei Kurokawa, Byung II Choi, Cheng-Chun Chang. IEEE Sensors Journal, 2011, 11(7): 1556.
[5] ZHENG Yu-quan, YU Bing-xi(郑玉权,禹秉熙). Journal of Remote Sensing(遥感学报), 2002, 6(1): 75.
[6] LIU Qing, ZHOU Jin-song, NIE Yun-feng(柳 青,周锦松,聂云峰). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2015, 35(4): 1142.
[7] Alivisatos A P. Science, 1996, 271(5251): 933.
[8] Bao J, Bawendi M G. Nature, 2015, 523: 67.
[9] Shi Li-Juan, Zhu Chun-Nan, He He, et al. RSC Advances, 2016, 6: 38183.
[10] Chang Cheng-Chun, Lee Heung-No. Optics Express, 2008, 16(2): 1056.
[11] Richad C Aster, Brian Borchers, Clifford H Thurber. Parameter Estimation and Inverse Problems. USA: Elsevier Academic Press,2013. 93.
[12] ZHANG Xian-da(张贤达). Matrix Analysis and Applications(矩阵分析与应用). Beijing: Tsinghua University Press(北京:清华大学出版社),2013. 326.
[13] TANG Guo-an, ZHANG You-shun, LIU Yong-mei(汤国安,张友顺,刘咏梅). Remote Sensing Digital Image Processing(遥感数字图像处理). Beijing: Science Press(北京:科学出版社),2004. 6.
[14] SHI Da-lian, Lü Qun-bo, CUI Yan(石大莲,吕群波,崔 燕). Acta Photonica Sinica(光子学报),2009, 38(6): 1530.
[15] Lü Qun-bo, YUAN Yan, XIANGLI Bin(吕群波,袁 艳,相里斌). Acta Photonica Sinica(光子学报),2008, 37(3): 573. |
[1] |
BAI Xi-lin1, 2, PENG Yue1, 2, ZHANG Xue-dong1, 2, GE Jing1, 2*. Ultrafast Dynamics of CdSe/ZnS Quantum Dots and Quantum
Dot-Acceptor Molecular Complexes[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 56-61. |
[2] |
LIU Zhen1*, LIU Li2*, FAN Shuo2, ZHAO An-ran2, LIU Si-lu2. Training Sample Selection for Spectral Reconstruction Based on Improved K-Means Clustering[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 29-35. |
[3] |
LI Yu1, ZHANG Ke-can1, PENG Li-juan2*, ZHU Zheng-liang1, HE Liang1*. Simultaneous Detection of Glucose and Xylose in Tobacco by Using Partial Least Squares Assisted UV-Vis Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 103-110. |
[4] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
[5] |
YI Min-na1, 2, 3, CAO Hui-min1, 2, 3*, LI Shuang-na-si1, 2, 3, ZHANG Zhu-shan-ying1, 2, 3, ZHU Chun-nan1, 2, 3. A Novel Dual Emission Carbon Point Ratio Fluorescent Probe for Rapid Detection of Lead Ions[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3788-3793. |
[6] |
LAI Niu, HUANG Qi-qiang, ZHANG Qin-yang, ZHANG Bo-wen, WANG Juan, YANG Jie, WANG Chong, YANG Yu, WANG Rong-fei*. Introduction to Perovskite Quantum Dots and Metal-Organic Frameworks and the Development of Composites[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3321-3329. |
[7] |
LIANG Jin-xing1, 2, 3, XIN Lei1, CHENG Jing-yao1, ZHOU Jing1, LUO Hang1, 3*. Adaptive Weighted Spectral Reconstruction Method Against
Exposure Variation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3330-3338. |
[8] |
YUAN Wei-dong1, 2, JU Hao2, JIANG Hong-zhe1, 2, LI Xing-peng2, ZHOU Hong-ping1, 2*, SUN Meng-meng1, 2. Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3419-3426. |
[9] |
SHEN Ying, WU Pan, HUANG Feng*, GUO Cui-xia. Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3629-3636. |
[10] |
YANG Lei1, 2, 3, ZHOU Jin-song1, 2, 3, JING Juan-juan1, 2, 3, NIE Bo-yang1, 3*. Non-Uniformity Correction Method for Splicing Hyperspectral Imager Based on Overlapping Field of View[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3582-3590. |
[11] |
WANG Wen-song1, PEI Chen-xi2, YANG Bin1*, WANG Zhi-xin2, QIANG Ke-jie2, WANG Ying1. Flame Temperature and Emissivity Distribution Measurement MethodBased on Multispectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3644-3652. |
[12] |
DONG Jian-jiang1, TIAN Ye1, ZHANG Jian-xing2, LUAN Zhen-dong2*, DU Zeng-feng2*. Research on the Classification Method of Benthic Fauna Based on
Hyperspectral Data and Random Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3015-3022. |
[13] |
JIANG Chun-xu1, 2, TAN Yong1*, XU Rong3, LIU De-long4, ZHU Rui-han1, QU Guan-nan1, WANG Gong-chang3, LÜ Zhong1, SHAO Ming5, CHENG Xiang-zheng5, ZHOU Jian-wei1, SHI Jing1, CAI Hong-xing1. Research on Inverse Recognition of Space Target Scattering Spectral
Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3023-3030. |
[14] |
WANG Yu-chen1, 2, KONG Ling-qin1, 2, 3*, ZHAO Yue-jin1, 2, 3, DONG Li-quan1, 2, 3*, LIU Ming1, 2, 3, HUI Mei1, 2. Hyperspectral Reconstruction From RGB Images for Tissue Oxygen
Saturation Assessment[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3193-3201. |
[15] |
JIA Hao1, 3, 4, ZHANG Wei-fang1, 3, LEI Jing-wei1, 3*, LI Ying-ying1, 3, YANG Chun-jing2, 3*, XIE Cai-xia1, 3, GONG Hai-yan1, 3, DING Xin-yu1, YAO Tian-yi1. Study on Infrared Fingerprint of the Classical Famous
Prescription Yiguanjian[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3202-3210. |
|
|
|
|