High spectral resolution imaging based on filter wheel dual camera system

被引:0
|
作者
Yu J. [1 ]
Zhao J. [1 ]
Cui G. [1 ]
Wu C. [1 ]
Zhu J. [1 ]
机构
[1] School of Electronics and Information, Hangzhou Dianzi University, Hangzhou
来源
Zhao, Jufeng (dabaozjf@hdu.edu.cn) | 1600年 / Science Press卷 / 42期
关键词
Dual cameras; Energy conservation; Filter wheel; High resolution; Interpolation compensation; Spectrum;
D O I
10.19650/j.cnki.cjsi.J2007230
中图分类号
学科分类号
摘要
The filter wheel-based spectral imaging system is widely used in spectral imaging, its spatial resolution is high, however, its spectral resolution is low. Aiming at this problem, a high spectral resolution imaging based on filter wheel dual camera system is introduced in this paper, meanwhile, a multi-spectral calculation and reconstruction method based on interpolation compensation is designed to achieve high spectral resolution and high spatial resolution imaging of the system. Firstly, the filter wheel dual camera imaging system is used to acquire the multi-spectral images and RGB images, and then the discrete spectral response curves are obtained from the multi-spectral images. Finally, according to the mapping relationship between the RGB three channel data and the spectral high-dimensional data, and the theorem of conservation of energy, the interpolation compensation of the spectral response curve is performed and the high spectral resolution imaging is achieved. Experiment results show that the proposed method can efficiently achieve the imaging with a spectral resolution of 5 nm even higher while maintaining the spatial resolution. The root mean square error between the reconstruction result and the true value is 0.017 1, the proposed method has high accuracy and robustness. © 2021, Science Press. All right reserved.
引用
收藏
页码:275 / 284
页数:9
相关论文
共 27 条
  • [1] DADON A, MANDELMILCH M, BEN-DOR E, Et al., Sequential PCA-based classification of mediterranean forest plants using airborne hyperspectral remote sensing, Remote Sensing, 11, 23, (2019)
  • [2] SCHWARTZ C R, EISMANN M T, CEDERQUIST J N, Et al., Thermal multispectral detection of military vehicles in vegetated and desert backgrounds, Proc. of SPIE, 2742, pp. 289-297, (1996)
  • [3] ZHANG ZH, JIANG J, FU J H, Et al., Multi-physiological mental fatigue detection based on functional near-infrared spectroscopy, Chinese Journal of Scientific Instrument, 38, 6, pp. 1345-1352, (2017)
  • [4] DONG J J, WU J ZH, LIU Q, Et al., Research on hyperspectral image detection method of imperfect wheat grains, Journal of Electronic Measurement and Instrumentation, 31, 7, pp. 1074-1080, (2017)
  • [5] IEHL J C, PEROCHE B., An adaptive spectral rendering with a perceptual control, Computer Graphics Forum, 19, 3, pp. 291-300, (2010)
  • [6] KITTLE D, CHOI K, WAGADARIKAR A, Et al., Multiframe image estimation for coded aperture snapshot spectral imagers, Applied Optics, 49, 36, pp. 6824-6833, (2010)
  • [7] LIN X, WETZSTEIN G, LIU Y, Et al., Dual-coded compressive hyperspectral imaging, Optics Letters, 39, 7, pp. 2044-2047, (2014)
  • [8] CAO X, DU H, TONG X, Et al., A prism-mask system for multispectral video acquisition, IEEE Transactions on Pattern Analysis & Machine Intelligence, 33, 12, pp. 2423-2435, (2011)
  • [9] CHEN L S., Research on multispectral video imaging system and algorithm, (2017)
  • [10] ZI CH D, LI Y Q, ZU Y X, Et al., Research on image alignment in multi-sensor spectral video imaging system, Infrared and Laser Engineering, 48, 6, pp. 276-283, (2019)