Author(s)

Zhongliang Luo ¹, Yingbiao Jia ¹

Affiliation(s)

¹ School of Information Science and Engineering, Shaoguan University, Shaoguan, China

Corresponding Author

Yingbiao Jia

ABSTRACT

Compressed sensing is suitable for remote hyperspectral imaging, as it can simplify the architecture of the onboard sensors. To reconstruct hyperspectral image from pushbroom compressive imaging, we present iterative prediction reconstruction architecture based on total variation in this paper. As the conventional total variation prior is not effective at capturing the correlation within spatial-spectral arrays, an improved weighted total variation is proposed. Experimental results run on raw data from AVIRIS confirm the validity of the proposed method.

KEYWORDS

hyperspectral compressive imaging, pushbroom, weighted total variation, iterative

CITE THIS PAPER

Yingbiao, J. and Zhongliang, L. (2016) Weighted Total Variation Iterative Reconstruction for Hyperspectral Pushbroom Compressive Imaging. Journal of Image Processing Theory and Applications (2016) 1: 6-10.

REFERENCES

[1] D Donoho. Compressed sensing. IEEE Trans. On Information Theory, 2006, 52(2): 489-509.
[2] R Willett, M F Duarte, M A Davenport, R G Baraniuk. Sparsity and Structure in Hyperspectral Imaging: Sensing, Reconstruction, and Target Detection. IEEE signal processing magazine, 2014,31(1):116-126.
[3] Jia Yingbiao, Feng Yan,Wang Zhongliang. Reconstructing Hyperspectral Images From Compressive Sensors via Exploiting Multiple Priors. Spectroscopy Letters.2015, 48(1):22-26.
[4] J. E. Fowler. Compressive Pushbroom and Whiskbroom Sensing for Hyperspectral Remote-Sensing Imaging. in Proceedings of the International Conference on Image Processing, Paris, France, October 2014: 684-688.
[5] Duncan T. Eason and Mark Andrews. Total Variation Regularization via Continuation to Recover Compressed Hyperspectral Images. IEEE transactions on image processing, 2015, 24(1):284-293.
[6] Simeon Kamdem Kuiteing, Giulio Coluccia, Alessandro Barducci, Mauro Barni, Enrico Magli: Compressive hyperspectral imaging using progressive total variation. ICASSP 2014: 7794-7798.

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