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Laser & Optoelectronics Progress, Volume. 62, Issue 2, 0237001(2025)

Turbulence-Blurred Target Restoration Algorithm with a Nonconvex Regularization Constraint

Xinggui Xu1、*, Hong Li1, Bing Ran2, Weihe Ren3, and Junrong Song1
Author Affiliations
  • 1The School of Information, Yunnan University of Finance and Economics, Kunming 650051, Yunnan , China
  • 2Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610054, Sichuan , China
  • 3The Institute of Beijing Space Electromechanical Research, Beijing 100039, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (15)
    Equations (29)
    References (29)
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    Figures & Tables(15)
    The framework of proposed algorithm

    Fig. 1. The framework of proposed algorithm

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    Component results of LatLRSD. (a)‒(d) Original turbulence blur image XO, low rank dictionary structural componentXZ*, hidden component L*X, and noise component E*

    Fig. 2. Component results of LatLRSD. (a)‒(d) Original turbulence blur image XO, low rank dictionary structural componentXZ*, hidden component L*X, and noise component E*

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    Iterative results of eigenvalues for LatLRSD. (a)‒(c) Singular value results corresponding to the noise component, low rank component, and hidden component in the low rank space decomposition at iteration numbers of #5, #25, #50, and #100

    Fig. 3. Iterative results of eigenvalues for LatLRSD. (a)‒(c) Singular value results corresponding to the noise component, low rank component, and hidden component in the low rank space decomposition at iteration numbers of #5, #25, #50, and #100

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    Schematic diagrams of regularization constraint conditions. (a) MC constraint; (b) scaled GMC constraint

    Fig. 4. Schematic diagrams of regularization constraint conditions. (a) MC constraint; (b) scaled GMC constraint

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    The turbulence-degraded targets restoration results of non-convex regularization. (a)‒(b) Restoration results for non-convex objective functions in two different scenarios at iteration numbers of #5, #25, #50, and #100

    Fig. 5. The turbulence-degraded targets restoration results of non-convex regularization. (a)‒(b) Restoration results for non-convex objective functions in two different scenarios at iteration numbers of #5, #25, #50, and #100

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    Original images. (a)‒(d) Flow field image, infrared turbulence image, long-range turbulence distortion image, and synthesized turbulence blur image

    Fig. 6. Original images. (a)‒(d) Flow field image, infrared turbulence image, long-range turbulence distortion image, and synthesized turbulence blur image

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    Restoration results of algorithm of reference [12]

    Fig. 7. Restoration results of algorithm of reference [12]

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    Restoration results of algorithm of reference [16]

    Fig. 8. Restoration results of algorithm of reference [16]

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    Restoration results of algorithm of reference [18]

    Fig. 9. Restoration results of algorithm of reference [18]

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    Restoration results of algorithm of reference [20]

    Fig. 10. Restoration results of algorithm of reference [20]

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    Restoration results of proposed algorithm

    Fig. 11. Restoration results of proposed algorithm

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    • Table 1. Restoration evaluation results of Fig.6(a)

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      Table 1. Restoration evaluation results of Fig.6(a)

      AlgorithmMESDICEISNR /dB
      Algorithm of reference ［12］7.50930.08120.24920.70829.2
      Algorithm of reference ［16］8.10240.04530.29150.40986.4
      Algorithm of reference ［18］8.24630.05050.30640.43576.2
      Algorithm of reference ［20］7.26550.04010.20270.51608.5
      Proposed algorithm8.71360.08870.39830.780410.3

    • Table 2. Restoration evaluation results of Fig.6(b)

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      Table 2. Restoration evaluation results of Fig.6(b)

      AlgorithmMESDICEISNR /dB
      Algorithm of reference ［12］7.40640.01520.22040.33324.7
      Algorithm of reference ［16］7.77120.01610.18640.38325.5
      Algorithm of reference ［18］8.05840.01870.22350.43217.7
      Algorithm of reference ［20］7.02310.01590.13170.20013.5
      Proposed algorithm8.15060.01970.23450.53348.0

    • Table 3. Restoration evaluation results of Fig.6(c)

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      Table 3. Restoration evaluation results of Fig.6(c)

      AlgorithmMESDICEISNR /dB
      Algorithm of reference ［12］5.13340.01860.20150.39572.8
      Algorithm of reference ［16］5.81540.01120.19640.33456.5
      Algorithm of reference ［18］6.18700.01070.23540.36408.5
      Algorithm of reference ［20］4.50870.01790.23690.34653.6
      Proposed algorithm6.49050.02360.33620.465411.1

    • Table 4. Restoration evaluation results of Fig.6(d)

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      Table 4. Restoration evaluation results of Fig.6(d)

      AlgorithmMESDICEISNR /dB
      Algorithm of reference ［12］7.53360.02060.22190.19413.7
      Algorithm of reference ［16］5.46320.02140.27630.24577.5
      Algorithm of reference ［18］5.61270.02100.28540.19808.1
      Algorithm of reference ［20］5.08700.02200.20010.19676.2
      Proposed algorithm6.09040.03080.35720.23507.2
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    Xinggui Xu, Hong Li, Bing Ran, Weihe Ren, Junrong Song. Turbulence-Blurred Target Restoration Algorithm with a Nonconvex Regularization Constraint[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0237001

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    Paper Information

    Category: Digital Image Processing

    Received: Feb. 20, 2024

    Accepted: Apr. 28, 2024

    Published Online: Jan. 3, 2025

    The Author Email:

    DOI:10.3788/LOP240707

    CSTR:32186.14.LOP240707

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology