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author:

Huang, Feng (Huang, Feng.) [1] | Lin, Peng (Lin, Peng.) [2] | Cao, Rongjin (Cao, Rongjin.) [3] | Zhou, Bin (Zhou, Bin.) [4] | Wu, Xianyu (Wu, Xianyu.) [5]

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EI

Abstract:

Conventional multispectral imaging systems based on bandpass filters struggle to record multispectral videos with high spatial resolutions because of their limited light efficiencies. This paper proposes a multi-aperture multispectral imaging system based on notch filters that overcomes this limitation by allowing light from most of the spectrum to pass through. Based on this imaging principle, a prototype multi-aperture multispectral imaging system comprising notch filters was built and demonstrated. Further, a dictionary learning- and total variation-based spectral super-resolution algorithm was developed to reconstruct spectral images. The simulation results obtained using public multispectral datasets showed that, compared to the dictionary learning-based spectral super-resolution algorithm, the proposed algorithm reconstructed the spectral information with a higher accuracy and removed noise, and the verification experiments confirmed the performance efficiency of the prototype system. The experimental results showed that the proposed imaging system can capture images with high spatial and spectral resolutions under low illumination conditions. The proposed algorithm improved the spectral resolution of the acquired data from 9 to 31 bands, and the average peak signal-to-noise ratio remained above 43 dB, which is 13 dB higher than those of the state-of-the-art coded aperture snapshot spectral imaging methods. Simultaneously, the frame rate of the imaging system was up to 5000 frames/s under natural daylight. © 2022 by the authors.

Keyword:

Bandpass filters Compressed sensing Efficiency Image reconstruction Image resolution Imaging systems Learning algorithms Notch filters Signal to noise ratio Spectral resolution Spectroscopy

Community:

  • [ 1 ] [Huang, Feng]School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 2 ] [Lin, Peng]School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 3 ] [Cao, Rongjin]School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 4 ] [Zhou, Bin]School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 5 ] [Wu, Xianyu]School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China

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Source :

Remote Sensing

Year: 2022

Issue: 16

Volume: 14

5 . 0

JCR@2022

4 . 2 0 0

JCR@2023

ESI HC Threshold:51

JCR Journal Grade:1

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

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