Dec. 2018

Article Contents

Chinese Optics> 2018> 11(6): 949-957.

TAN Cui-mei, XU Ting-fa, MA Xu, ZHANG Yu-han, WANG Xi, YAN Ge. Graph-spectral hyperspectral video restoration based on compressive sensing[J]. Chinese Optics, 2018, 11(6): 949-957. doi: 10.3788/CO.20181106.0949

Citation:

TAN Cui-mei, XU Ting-fa, MA Xu, ZHANG Yu-han, WANG Xi, YAN Ge. Graph-spectral hyperspectral video restoration based on compressive sensing[J].Chinese Optics, 2018, 11(6): 949-957.doi:10.3788/CO.20181106.0949

Citation:

TAN Cui-mei, XU Ting-fa, MA Xu, ZHANG Yu-han, WANG Xi, YAN Ge. Graph-spectral hyperspectral video restoration based on compressive sensing[J].Chinese Optics, 2018, 11(6): 949-957.doi:10.3788/CO.20181106.0949

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Graph-spectral hyperspectral video restoration based on compressive sensing

doi:10.3788/CO.20181106.0949

School of Optoelectronics, Key Laboratory of the Ministry of Education for Opto-Electronic Imaging Technology and System, Beijing Institute of Technology, Beijing 100081, China

Funds:

Science Instrument Program of the National Natural Science Foundation of China61527802

More Information

Corresponding author:XU Ting-fa, E-mail:ciom_xtf1@bit.edu.cn; MA Xu, E-mail:maxu@bit.edu.cn
Received Date:17 Nov 2017
Rev Recd Date:07 Jan 2018
Publish Date:01 Dec 2018

Abstract

Abstract

In this paper, a new graph-spectral hyperspectral video restoration method regarding the imaging characteristics of dynamic scenes recorded by liquid crystal tunable filter hyperspectral imaging system is proposed. Firstly, the hyperspectral image of the moving foreground target is obtained by the foreground target detection, and the moving foreground target is separated from the background region. Then the background region is divided into the motion region which is obscured by the foreground target and the still region which is not obscured by the foreground target according to the foreground target detection result. Based on the correlation of the spatial dimension and spectral dimension of the hyperspectral image, dictionary learning is performed on the still region to obtain sparse prior information. Combined with compressed sensing theory for motion region recovery, a complete background region hyperspectral image is obtained. Finally, the moving foreground target hyperspectral image is combined with the background region hyperspectral image to obtain a hyperspectral video image. The experimental results show that the proposed method of hyperspectral video image restoration outperforms the existing algorithm in terms of peak signal-to-noise ratio and visual effect, and the peak signal-to-noise ratio is increased by an average of more than 5 dB.
- hyperspectral video restoration,
- liquid crystal tunable filter,
- object detection,
- compressive sensing,
- dictionary learning

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References (20)

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