• 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
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    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
    [in Chinese]
    Fig. 1. [in Chinese]
    Spectral imaging based on spatial compressive sampling and spectral KL transform
    Fig. 2. Spectral imaging based on spatial compressive sampling and spectral KL transform
    SNR of each band of spectral image
    Fig. 3. SNR of each band of spectral image
    PSNR of each band of reconstructed spectral image for 2D-CRPG algorithm with differentRspa. (a)K=10; (b)K=150
    Fig. 4. PSNR of each band of reconstructed spectral image for 2D-CRPG algorithm with different Rspa . (a) K =10; (b) K =150
    Two-dimensional image reconstruction effect of 2D-CRPG algorithm with differentRspaunder condition ofK=150
    Fig. 5. Two-dimensional image reconstruction effect of 2D-CRPG algorithm with different Rspa under condition of K= 150
    PSNR of each band of reconstructed spectral image for 2D-CRPG algorithm with differentKvalues. (a)Rspa=40%; (b)Rspa=90%
    Fig. 6. PSNR of each band of reconstructed spectral image for 2D-CRPG algorithm with different K values. (a) Rspa =40%; (b) Rspa =90%
    PSNR of each band of reconstructed spectral image for different algorithms. (a)Rspa=40%; (b)Rspa=50%; (c)Rspa=60%; (d)Rspa=70%; (e)Rspa=80%; (f)Rspa=90%
    Fig. 7. PSNR of each band of reconstructed spectral image for different algorithms. (a) Rspa =40%; (b) Rspa =50%; (c) Rspa =60%; (d) Rspa =70%; (e) Rspa =80%; (f) Rspa =90%
    False color images of reconstructed spectral images for different algorithms withRspa=40% (bands 250, 222, 10). (a) Original image; (b) BCS,aAPSNR=28.86 dB; (c) CSC,aAPSNR=27.08 dB; (d) 2D-OMP,aAPSNR=26.41 dB; (e) Kro-OMP,aAPSNR=19.05 dB; (f) 2D-CRPG,aAPSNR=33.14 dB
    Fig. 8. False color images of reconstructed spectral images for different algorithms with Rspa =40% (bands 250, 222, 10). (a) Original image; (b) BCS, aAPSNR =28.86 dB; (c) CSC, aAPSNR =27.08 dB; (d) 2D-OMP, aAPSNR =26.41 dB; (e) Kro-OMP, aAPSNR =19.05 dB; (f) 2D-CRPG, aAPSNR =33.14 dB
    False color images of reconstructed spectral images for different algorithms withRspa=90% (bands 250, 222, 10). (a) Original image; (b) BCS,aAPSNR=34.54 dB; (c) CSC,aAPSNR=32.95 dB; (d) 2D-OMP,aAPSNR=35.41 dB; (e) Kro-OMP,aAPSNR=23.10 dB; (f) 2D-CRPG,aAPSNR=42.81 dB
    Fig. 9. False color images of reconstructed spectral images for different algorithms with Rspa =90% (bands 250, 222, 10). (a) Original image; (b) BCS, aAPSNR =34.54 dB; (c) CSC, aAPSNR =32.95 dB; (d) 2D-OMP, aAPSNR =35.41 dB; (e) Kro-OMP, aAPSNR =23.10 dB; (f) 2D-CRPG, aAPSNR =42.81 dB
    APSNR of different algorithms with differentRspa
    Fig. 10. APSNR of different algorithms with different Rspa
    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
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