• Acta Photonica Sinica
  • Vol. 49, Issue 8, 0810001 (2020)
Xiao-jie YE1, Guang-mang CUI1、2, Ju-feng ZHAO1、2, and Li-yao ZHU1
Author Affiliations
  • 1School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China
  • 2Zhejiang Provincial Key Lab of Equipment Electronics, Hangzhou 310018, China
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    DOI: 10.3788/gzxb20204908.0810001 Cite this Article
    Xiao-jie YE, Guang-mang CUI, Ju-feng ZHAO, Li-yao ZHU. Motion Blurred Image Restoration Based on Complementary Sequence Pair Using Fluttering Shutter Imaging[J]. Acta Photonica Sinica, 2020, 49(8): 0810001 Copy Citation Text show less
    Comparison of different imaging modes and restoration results.
    Fig. 1. Comparison of different imaging modes and restoration results.
    MTF curve of Golay sequence pair
    Fig. 2. MTF curve of Golay sequence pair
    Overall flow chart
    Fig. 3. Overall flow chart
    Hardware simulation platform physical figure
    Fig. 4. Hardware simulation platform physical figure
    MTF curve of different combination code pairs
    Fig. 5. MTF curve of different combination code pairs
    Images from LIVE database
    Fig. 6. Images from LIVE database
    The objective evaluation of different images restored under different imaging modes.
    Fig. 7. The objective evaluation of different images restored under different imaging modes.
    Restoration simulation under different code modes
    Fig. 8. Restoration simulation under different code modes
    Comparison of target image restoration under different code modes
    Fig. 9. Comparison of target image restoration under different code modes
    Comparison of signboard image restoration under different code modes
    Fig. 10. Comparison of signboard image restoration under different code modes
    A part of clear images of typical moving objects
    Fig. 11. A part of clear images of typical moving objects
    Restoration results of typical moving objects and enlarged display of local areas
    Fig. 12. Restoration results of typical moving objects and enlarged display of local areas
    Input:f1f2h1h2λ > 0
    Output:g
    Initial value:order h=h1, gf1, i=1.
    While “i ≤ 2”,do
        While“Non-convergence”
        1)Fixed g,solve yi by minimization y
        2)Fixed y,solve gi by minimization g
        End do;
        1)i=i+1;
    End do
    Table 1. Recovery algorithm flow
    MethodRandom code pairSymmetric code pairJeon's complementary code pairProposed code pair
    MIN-8.15-4.43.524.04
    VAR6.838.036.453.72
    Table 2. Minimum value and variance of combined MTF curve
    MethodTraditionalS codeRandom code pairSymmetric code pairJeon's complementary code pairProposed code pair
    SSIM0.819 70.850 80.866 10.853 30.896 7
    SNR26.276 727.631 227.961 226.551 328.640 8
    Time/s0.521.221.91.2
    Table 3. Objective evaluation of simulation restoration in different code modes
    MethodTraditional codeRandom code pairSymmetric code pairProposed code pair
    SSIM0.723 60.760 80.825 40.896 3
    SNR19.754 320.283 117.197 722.490 5
    Table 4. Objective evaluation of target image restoration under different code modes
    MethodTraditional codeRandom code pairSymmetric code pairProposed code pair
    SSIM0.860 80.883 70.899 20.920 6
    SNR19.824 121.684 115.224 522.229 2
    Table 5. Objective evaluation of signboard image restoration under different code modes
    Evaluation methodBlurred imageThe results of this methodImprove index rate
    SSIM0.787 90.943 919.8%
    SNR19.936 126.895 734.9%
    Table 6. Improvement value of evaluation index of restoration results in Fig. 12
    Xiao-jie YE, Guang-mang CUI, Ju-feng ZHAO, Li-yao ZHU. Motion Blurred Image Restoration Based on Complementary Sequence Pair Using Fluttering Shutter Imaging[J]. Acta Photonica Sinica, 2020, 49(8): 0810001
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