Yingbiao Jia
ABSTRACT
Hyperspectral imaging systems can benefit from compressed sensing to reduce the size demand of sensor array. A new reconstruction algorithm is presented to recover the hyperspectral images from limited compressive measurements, exploiting the inherent spatial local smoothness prior, spatial nonlocal self-similarity prior and adjacent spectral similarity prior through joint regularization. The reconstruction process is solved with the help of augmented lagrangian multipliers and alternating direction method. The experimental results show that our method exhibits its superiority over other traditional methods with higher reconstruction quality at the same measurement rates.
- M. Gehm, R. John, D. Brady, R. Willett, and T. Schulz. 2007. Single-shot compressive spectral imaging with a dual-disperser architecture. Optics Express, 15,21(Oct. 2007), 14 013--14 027.Google Scholar
- T. Sun and K. Kelly.2009.Compressive sensing hyperspectral imager. Frontiers in Optics 2009/Laser Science XXV/Fall2009 OSA Optics & Photonics Technical Digest p. CTuA5.Google Scholar
- Xing Lin, Yebin Liu, Jiamin Wu, and Qionghai Dai.2014. Spatial-spectral encoded compressive hyperspectral imaging. ACM Transactions on Graphics, 33, 6 (Nov 2014), 1-11. Google ScholarDigital Library
- P. Meza, I. Ortiz, E. Vera, and J. Martinez. 2018. Compressive hyperspectral imaging recovery by spatial-spectral non-local means regularization. Optics Express, 26,6, (Mar. 2018), 7043--7055.Google Scholar
- Jia Yingbiao, Feng Yan, and Wang Zhongliang.2015. Reconstructing hyperspectral images from compressive sensors via exploiting multiple priors. Spectroscopy Letters. 48, 1 (Jan, 2015), 22--26.Google Scholar
- X. Shu and N. Ahuja. 2010. Hybrid compressive sampling via a new total variation tvl1. In Proceedings of 11th European Conference on computer Vision (Crete, Greece, September 05-11 2010). 393--404. Google ScholarDigital Library
- Jian Zhang, Shaohui Liu, Ruiqin Xiong, Siwei Ma, and Debin Zhao. 2013. Improved total variation based image compressive sensing recovery by nonlocal regularization. in Proceedings of IEEE International Symposium on Circuits and Systems. (May 19-22 2013). 2836--2839.Google Scholar
- Kan Chang, Baoxin Li. 2015.Joint modeling and reconstruction of a compressively-sensed set of correlated images. Journal of Visual Communication & Image Representation, 33(Nov. 2015), 286--300. Google ScholarDigital Library
- Zhonghua Yan. 2018. Artificial Bee Colony Constrained Optimization Algorithm with Hybrid Discrete Variables and its Application. Acta Electronica Malaysia, 2(1) 18--20.Google ScholarCross Ref
Index Terms
- Compressive Hyperspectral Imaging Reconstruction by Spatial and Spectral Joint Prior
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