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16 September 2011 Compressed hyperspectral image sensing with joint sparsity reconstruction
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Recent compressed sensing (CS) results show that it is possible to accurately reconstruct images from a small number of linear measurements via convex optimization techniques. In this paper, according to the correlation analysis of linear measurements for hyperspectral images, a joint sparsity reconstruction algorithm based on interband prediction and joint optimization is proposed. In the method, linear prediction is first applied to remove the correlations among successive spectral band measurement vectors. The obtained residual measurement vectors are then recovered using the proposed joint optimization based POCS (projections onto convex sets) algorithm with the steepest descent method. In addition, a pixel-guided stopping criterion is introduced to stop the iteration. Experimental results show that the proposed algorithm exhibits its superiority over other known CS reconstruction algorithms in the literature at the same measurement rates, while with a faster convergence speed.
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Haiying Liu, Yunsong Li, Jing Zhang, Juan Song, and Pei Lv "Compressed hyperspectral image sensing with joint sparsity reconstruction", Proc. SPIE 8157, Satellite Data Compression, Communications, and Processing VII, 815703 (16 September 2011);

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