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Multidimensional dictionary learning algorithm for compressive sensing-based hyperspectral imaging

[+] Author Affiliations
Rongqiang Zhao, Qiang Wang, Yi Shen

Harbin Institute of Technology, Department of Control Science and Engineering, No. 92, Xidazhi Street, Harbin 150000, China

Jia Li

China Electronics Technology Group Corporation 54th Research Institute, No. 589, Zhongshanxi Road, Shijiazhuang 050002, China

J. Electron. Imaging. 25(6), 063013 (Dec 02, 2016). doi:10.1117/1.JEI.25.6.063013
History: Received July 16, 2016; Accepted November 11, 2016
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Abstract.  The sparsifying representation plays a significant role in compressive sensing (CS)-based hyperspectral (HS) imaging. Training the dictionaries for each dimension from HS samples is very beneficial to accurate reconstruction. However, the tensor dictionary learning algorithms are limited by a great amount of computation and convergence difficulties. We propose a least squares (LS) type multidimensional dictionary learning algorithm for CS-based HS imaging. We develop a practical method for the dictionary updating stage, which avoids the use of the Kronecker product and thus has lower computation complexity. To guarantee the convergence, we add a pruning stage to the algorithm to ensure the similarity and relativity among data in the spectral dimension. Our experimental results demonstrated that the dictionaries trained using the proposed algorithm performed better at CS-based HS image reconstruction than those trained with traditional LS-type dictionary learning algorithms and the commonly used analytical dictionaries.

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Citation

Rongqiang Zhao ; Qiang Wang ; Yi Shen and Jia Li
"Multidimensional dictionary learning algorithm for compressive sensing-based hyperspectral imaging", J. Electron. Imaging. 25(6), 063013 (Dec 02, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.6.063013


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