• Acta Optica Sinica
  • Vol. 38, Issue 5, 0530004 (2018)
Yidong Tang, Shucai Huang*, and Da Huang
Author Affiliations
  • College of Air and Missile Defense, Air Force Engineering University, Xi'an, Shaanxi 710051, China
  • show less
    DOI: 10.3788/AOS201838.0530004 Cite this Article Set citation alerts
    Yidong Tang, Shucai Huang, Da Huang. Spectral Imaging and Reconstruction Based on Spatial Compressive Sampling and Spectral Karhunen-Loève Transform[J]. Acta Optica Sinica, 2018, 38(5): 0530004 Copy Citation Text show less
    References

    [1] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 52, 1289-1306(2006).

    [2] Candes E J, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 52, 489-509(2006).

    [3] Wang Q, Ma L L, Li C R et al. Improved method of dictionary atom selection in compressive sensing spectral reconstruction[J]. Acta Optica Sinica, 36, 0930002(2016).

    [4] Jing N, Bi W H, Hu Z P et al. A survey on dynamic compressed sensing[J]. Acta Automatica Sinica, 41, 22-37(2015).

    [5] Tan S Y, Liu Z T, Li E R et al. Hyperspectral compressed sensing based on prior images constrained[J]. Acta Optica Sinica, 35, 0811003(2015).

    [6] Yan J W, Liu L, Qu X B[M]. Compressive sensing and its applications, 67-89(2015).

    [7] Rivenson Y, Stern A. Compressed imaging with a separablesensing operator[J]. IEEE Signal Processing Letters, 16, 449-452(2009). http://ieeexplore.ieee.org/document/4801726/

    [8] Wu Q, Zhou L J, Yin J F[M]. Matrix analysis, 54-58(2017).

    [9] Li Z L. Study on image compressive sensing reconstruction algorithms[D]. Beijing: Beijing Jiaotong University, 95-109(2012).

    [10] Lu G. Block compressed sensing of natural images. [C]∥Proceedings of the 15 th International Conference on Digital Signal Processing, 403-406(2007).

    [11] Cen Y G, Chen X F, Cen L H et al. Compressed sensing based on the single layer wavelet transform for image processing[J]. Journal on Communications, 31, 52-55(2010).

    [12] Fang Y, Wu J J, Huang B. 2D sparse signal recovery via 2D orthogonal matching pursuit[J]. Science China: Information Sciences, 55, 889-897(2012). http://www.cqvip.com/QK/84009A/201204/41203029.html

    [13] Ghaffari A, Babaie-Zadeh M, Jutten C. Sparse decomposition of two dimensional signals. [C]∥Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, 3157-3160(2009).

    [14] Wimalajeewa T, Eldar Y C. -11-11)[2017-11-20]. http: ∥arxiv., org/abs/1311, 2448(2013).

    [15] Liao L, Zhang Y N, Zhang C. 2DCS: two dimensional random underdetermined projection for image representation and classification. [C]∥Proceedings of the International Conference on Multimedia Technology, 1-5(2011).

    [16] Daubechies I, Fornasier M, Loris I. Accelerated projected gradient method for linear inverse problems with sparsity constraints[J]. Journal of Fourier Analysis and Applications, 14, 764-792(2008). http://link.springer.com/article/10.1007/s00041-008-9039-8

    [17] Tang Y D. Research on detection and classification method for compressive spectral imaging[D]. Xi’an: Air Force Engineering University, 75-89(2017).

    Yidong Tang, Shucai Huang, Da Huang. Spectral Imaging and Reconstruction Based on Spatial Compressive Sampling and Spectral Karhunen-Loève Transform[J]. Acta Optica Sinica, 2018, 38(5): 0530004
    Download Citation