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Weighted Total Variation Iterative Reconstruction for Hyperspectral Pushbroom Compressive Imaging

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DOI: 10.23977/jipta.2016.11002 | Downloads: 51 | Views: 3215

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

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