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Acta Optica Sinica, Volume. 38, Issue 11, 1110003(2018)

Color Compensation Based on Bright Channel and Fusion for Underwater Image Enhancement

Chenggang Dai1,2,3、*, Mingxing Lin1,2,3、*, Zhen Wang1,2,3, Dong Zhang4, and Zhiguang Guan5
Author Affiliations
  • 1 School of Mechanical Engineering, Shandong University, Jinan, Shandong 250061, China
  • 2 National Demonstration Center for Experimental Mechanical Engineering, School of Mechanical Engineering, Shandong University, Jinan, Shandong 250061, China
  • 3 Key Laboratory of High-efficiency and Clean Mechanical Manufacture of Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan, Shandong 250061, China
  • 4 Institute of Automation Shangdong Academy of Sciences, Jinan, Shandong 250013, China
  • 5 School of Construction Machinery, Shandong Jiaotong University, Jinan, Shandong 250023, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (12)
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    Cited By (16)
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    Figures & Tables(12)
    Flow chart of the proposed method

    Fig. 1. Flow chart of the proposed method

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    Schematic of underwater light absorption

    Fig. 2. Schematic of underwater light absorption

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    Contrast color-compensated image and original image. (a) Original image; (b) red channel; (c) green channel; (d) blue channel; (e) color compensated image based on bright channel; (f) color compensated red channel; (g) color compensated green channel; (h) color compensated blue channel

    Fig. 3. Contrast color-compensated image and original image. (a) Original image; (b) red channel; (c) green channel; (d) blue channel; (e) color compensated image based on bright channel; (f) color compensated red channel; (g) color compensated green channel; (h) color compensated blue channel

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    Schematic of underwater optical imaging

    Fig. 4. Schematic of underwater optical imaging

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    Adaptive contrast-stretched image. (a) Original image; (b) contrast-stretched image

    Fig. 5. Adaptive contrast-stretched image. (a) Original image; (b) contrast-stretched image

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    Weight maps. (a) Color-compensated image (input1); (b) normalized weight maps for input1; (c) contrast-stretched image (input2); (d) normalized weight maps for input2

    Fig. 6. Weight maps. (a) Color-compensated image (input1); (b) normalized weight maps for input1; (c) contrast-stretched image (input2); (d) normalized weight maps for input2

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    Color recovery test. (a) Standard color checker; (b) original image; (c) CALHE method; (d) DCP method; (e) MSRCR method; (f) proposed method

    Fig. 7. Color recovery test. (a) Standard color checker; (b) original image; (c) CALHE method; (d) DCP method; (e) MSRCR method; (f) proposed method

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    Color distortion image test. (a) Original image; (b) CALHE method; (c) DCP method; (d) MSRCR method; (e) proposed method

    Fig. 8. Color distortion image test. (a) Original image; (b) CALHE method; (c) DCP method; (d) MSRCR method; (e) proposed method

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    Hazed image test. (a) Group of original images; (b) CALHE method; (c) DCP method; (d) MSRCR method; (e) proposed method

    Fig. 9. Hazed image test. (a) Group of original images; (b) CALHE method; (c) DCP method; (d) MSRCR method; (e) proposed method

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    Application test. (a) Feature point matching in the original image; (b) feature point matching in image processed using the proposed method

    Fig. 10. Application test. (a) Feature point matching in the original image; (b) feature point matching in image processed using the proposed method

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    • Table 1. Quality evaluation of underwater images

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      Table 1. Quality evaluation of underwater images

      Image setCLAHEDCPMSRCRProposed method
      MSEPSNRMSEPSNRMSEPSNRMSEPSNR
      Image117334.75.7421286.117.03830025.73.35613756.76.746
      Image225656.74.039131.226.95040382.62.06910960.77.732
      Image310310.87.99810852.37.7765125.311.0345098.911.189
      Image411414.67.55611241.37.6237356.39.4642926.1813.468
      Image56096.610.2805987.610.3588335.18.9223926.312.191
      Image69868.38.1856853.79.7729747.08.2425490.110.735
      Image77516.79.37117752.85.6387423.79.4253174.013.115

    • Table 2. Number of feature points matching contrast in original images and images processed using our method

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      Table 2. Number of feature points matching contrast in original images and images processed using our method

      Image setOriginal imageProposed method
      First group428
      Second group819
      Third group656
      Forth group717
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    Chenggang Dai, Mingxing Lin, Zhen Wang, Dong Zhang, Zhiguang Guan. Color Compensation Based on Bright Channel and Fusion for Underwater Image Enhancement[J]. Acta Optica Sinica, 2018, 38(11): 1110003

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    Paper Information

    Category: Image Processing

    Received: Mar. 29, 2018

    Accepted: Jun. 13, 2018

    Published Online: May. 9, 2019

    The Author Email:

    DOI:10.3788/AOS201838.1110003

    Topics

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