Fast OMP reconstruction for compressive hyperspectral imaging using joint spatial-spectral sparsity model

Haiying Liu; Rongli Chen; Yajun Wang; Pei Lv

doi:10.1117/12.2504270

15 November 2018 Fast OMP reconstruction for compressive hyperspectral imaging using joint spatial-spectral sparsity model

Haiying Liu, Rongli Chen, Yajun Wang, Pei Lv

Author Affiliations +

Proceedings Volume 10964, Tenth International Conference on Information Optics and Photonics; 109640J (2018) https://doi.org/10.1117/12.2504270
Event: Tenth International Conference on Information Optics and Photonics (CIOP 2018), 2018, Beijing, China

Abstract

Hyperspectral imaging typically produces huge data volume that demands enormous computational resources in terms of storage, computation and transmission, particularly when real-time processing is desired. In this paper, we study a lowcomplexity scheme for hyperspectral imaging completely bypassing high-complexity compression task. In this scheme, compressive hyperspectral data are acquired directly by a device similar to the single-pixel camera based on the principle of compressive sensing (CS). To decode the compressive data, we propose a flexible recovery strategy by taking advantage of the joint spatial-spectral correlation model of hyperspectral images. Moreover, a thorough investigation is analytically conducted on compressive hyperspectral data and we find that the compressive data still have strong spectral correlation. To make the recovery more accurate, an adaptive spectral band reordering algorithm is directly added to the compressive data before the reconstruction by making best use of spectral correlation. The real hyperspectral images are tested to demonstrate the feasibility and efficiency of the proposed algorithm. Experimental results indicate that the proposed recover algorithm can speed up the reconstruction process with reliable recovery quality.

Citation Download Citation

Haiying Liu, Rongli Chen, Yajun Wang, and Pei Lv "Fast OMP reconstruction for compressive hyperspectral imaging using joint spatial-spectral sparsity model", Proc. SPIE 10964, Tenth International Conference on Information Optics and Photonics, 109640J (15 November 2018); https://doi.org/10.1117/12.2504270

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