17 December 2016 Compressed sensing of hyperspectral images based on scrambled block Hadamard ensemble
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Abstract
A fast measurement matrix based on scrambled block Hadamard ensemble for compressed sensing (CS) of hyperspectral images (HSI) is investigated. The proposed measurement matrix offers several attractive features. First, the proposed measurement matrix possesses Gaussian behavior, which illustrates that the matrix is universal and requires a near-optimal number of samples for exact reconstruction. In addition, it could be easily implemented in the optical domain due to its integer-valued elements. More importantly, the measurement matrix only needs small memory for storage in the sampling process. Experimental results on HSIs reveal that the reconstruction performance of the proposed measurement matrix is comparable or better than Gaussian matrix and Bernoulli matrix using different reconstruction algorithms while consuming less computational time. The proposed matrix could be used in CS of HSI, which would save the storage memory on board, improve the sampling efficiency, and ameliorate the reconstruction quality.
Li Wang, Li Wang, Yan Feng, Yan Feng, } "Compressed sensing of hyperspectral images based on scrambled block Hadamard ensemble," Journal of Electronic Imaging 25(6), 063021 (17 December 2016). https://doi.org/10.1117/1.JEI.25.6.063021 . Submission: Received: 17 July 2016; Accepted: 21 November 2016
Received: 17 July 2016; Accepted: 21 November 2016; Published: 17 December 2016
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