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Laser & Optoelectronics Progress, Volume. 61, Issue 18, 1837001(2024)

Detail-Preserving Multi-Exposure Image Fusion Based on Adaptive Weight

Ruihong Wen1,2,3, Chunyu Liu1,3、*, Shuai Liu1,3, Meili Zhou1,3, and Yuxin Zhang1,3
Author Affiliations
  • 1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, Jilin, China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
  • 3Key Laboratory of Space-based Dynamic & Rapid Optical Imaging Technology, Chinese Academy of Sciences,Changchun 130033, Jilin, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (12)
    Equations (26)
    References (22)
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    Figures & Tables(12)
    Relationship between image information entropy and exposure time in low dynamic range scene

    Fig. 1. Relationship between image information entropy and exposure time in low dynamic range scene

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    Comparison before and after guided filtering

    Fig. 2. Comparison before and after guided filtering

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    The process of proposed method

    Fig. 3. The process of proposed method

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    Laplace pyramid reconstruction

    Fig. 4. Laplace pyramid reconstruction

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    Partial fusion results of proposed algorithm

    Fig. 5. Partial fusion results of proposed algorithm

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    Fusion results of source image sequence Cave by different algorithms. (a) Multi-exposure image sequence Cave; (b) algorithm 1; (c) algorithm 2; (d) algorithm 3; (e) algorithm 4; (f) algorithm 5; (g) algorithm 6; (h) algorithm 7; (i) proposed algorithm

    Fig. 6. Fusion results of source image sequence Cave by different algorithms. (a) Multi-exposure image sequence Cave; (b) algorithm 1; (c) algorithm 2; (d) algorithm 3; (e) algorithm 4; (f) algorithm 5; (g) algorithm 6; (h) algorithm 7; (i) proposed algorithm

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    Fusion results of source image sequence Lamp by different algorithms. (a) Multi-exposure image sequence Lamp(b) algorithm 1; (c) algorithm 2;(d) algorithm 3; (e) algorithm 4; (f) algorithm 5; (g) algorithm 6; (h) algorithm 7; (i) proposed algorithm

    Fig. 7. Fusion results of source image sequence Lamp by different algorithms. (a) Multi-exposure image sequence Lamp(b) algorithm 1; (c) algorithm 2;(d) algorithm 3; (e) algorithm 4; (f) algorithm 5; (g) algorithm 6; (h) algorithm 7; (i) proposed algorithm

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    Fusion results of source image sequence Mountain Garden by different algorithms. (a) Multi-exposure image sequence Mountain garden; (b) algorithm 1;(c) algorithm 2; (d) algorithm 3; (e) algorithm 4; (f) algorithm 5; (g) algorithm 6; (h) algorithm 7; (i) proposed algorithm

    Fig. 8. Fusion results of source image sequence Mountain Garden by different algorithms. (a) Multi-exposure image sequence Mountain garden; (b) algorithm 1;(c) algorithm 2; (d) algorithm 3; (e) algorithm 4; (f) algorithm 5; (g) algorithm 6; (h) algorithm 7; (i) proposed algorithm

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    Fusion and ablation experimental results of multi-exposure image sequence Venice. (a) Multi-exposure image sequence Venice; (b) fusion result of proposed algorithm; (c) fusion result without guided filtering; (d) fusion result without structural weight; (e) fusion result without saturation weight; (f) fusion result without exposure weight

    Fig. 9. Fusion and ablation experimental results of multi-exposure image sequence Venice. (a) Multi-exposure image sequence Venice; (b) fusion result of proposed algorithm; (c) fusion result without guided filtering; (d) fusion result without structural weight; (e) fusion result without saturation weight; (f) fusion result without exposure weight

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    Relationship between MEF-SSIM and Gaussian standard deviation

    Fig. 10. Relationship between MEF-SSIM and Gaussian standard deviation

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    • Table 1. Comparison of objective indicator average values of different algorithms on three datasets

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      Table 1. Comparison of objective indicator average values of different algorithms on three datasets

      DatasetEvaluationAlgorithm
      1234567Proposed
      Dataset 1MEF-SSIM0.9790.9720.9830.9780.9660.9830.9750.983
      CE1.8841.7071.6591.6511.6071.9191.8111.944
      Dataset 2MEF-SSIM0.9740.9330.9790.9230.9560.9780.9680.977
      CE2.3442.2492.0982.2112.0952.4172.3312.479
      Dataset 3MEF-SSIM0.9870.9620.9880.9640.9710.9870.9800.988
      CE2.5982.4752.1732.3211.8352.2162.1802.601

    • Table 2. Comparison of indicators for different element combinations in three datasets

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      Table 2. Comparison of indicators for different element combinations in three datasets

      DatasetMEF-SSIMCE
      SEGCEGCSGCSECSEGSEGCEGCSGCSECSEG
      Dataset 10.9750.9810.9820.9820.9831.8121.9121.8741.8971.944
      Dataset 20.9690.9740.9760.9770.9772.2272.4692.4372.4712.479
      Dataset 30.9780.9850.9860.9870.9882.5522.5072.5172.5992.601
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    Ruihong Wen, Chunyu Liu, Shuai Liu, Meili Zhou, Yuxin Zhang. Detail-Preserving Multi-Exposure Image Fusion Based on Adaptive Weight[J]. Laser & Optoelectronics Progress, 2024, 61(18): 1837001

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    Paper Information

    Category: Digital Image Processing

    Received: Nov. 23, 2023

    Accepted: Jan. 18, 2024

    Published Online: Sep. 14, 2024

    The Author Email: Chunyu Liu (mmliucy@163.com)

    DOI:10.3788/LOP232552

    CSTR:32186.14.LOP232552

    Topics

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