Laser & Optoelectronics Progress, Volume. 59, Issue 16, 1601002(2022)

Underwater Optical Image Enhancement Based on Color Constancy and Multiscale Wavelet

Xiaoqi Wang1,2, Xuanzhi Zhao1,2、*, and Zengli Liu1,2
Author Affiliations
  • 1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, Yunnan , China
  • 2Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming 650500, Yunnan , China
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    Figures & Tables(13)
    Flow chart of proposed algorithm
    Color correction results.(a) original images; (b) pseudo color images of original images;(c)color correction images;(d)pseudo color images based on color correction
    Comparison of different wavelet decomposition levels. (a) Original image; (b) level 0; (c) level 2; (d) level 4; (e) details of level 4
    Comparison of underwater optical image clarity effect. (a) Original image; (b) dark channel prior dehaze; (c) multiscale wavelet dehaze
    2D gamma correction histogram contrast map. (a) Original image; (b) illumination component map of original image; (c) histogram before correction; (d) 2D gamma correction map; (e) illumination component map of 2D gamma correction image; (f) histogram after correction;
    Comparison of sharpen. (a) Original image; (b) gray scale image of original image; (c) edge detail of original image; (d) sharpened image; (e) gray scale image of sharpened image; (f) edge detail of sharpened image
    2D gamma weightmap. (a) 2D gamma correction result;(b)luminance weightmap;(c)chromatic weightmap;(d)saliency weightmap
    Sharpen weightmap. (a) Sharpen result; (b) luminance weightmap;(c)chromatic weightmap;(d)saliency weightmap
    Underwater optical image enhancement results based on different algorithms. (a) Original image;(b)algorithm in reference[6];(c)algorithm in reference[7];(d)algorithm in reference[8];(e)algorithm in reference[9];(f)proposed algorithm
    Application test results. (a) Original image matching results; (b) enhanced image matching results
    Evaluation results of low illumination image
    Canny edge detection comparison results. (a) Original image; (b) Canny operator detection results of original image; (c) enhanced image; (d) Canny operator detection results of enhanced image
    • Table 1. Comparison of evaluation indexes of images processed by different algorithms

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      Table 1. Comparison of evaluation indexes of images processed by different algorithms

      ImageAlgorithm in reference[9Proposed Method
      UCIQEUIQMAGUCIQEUIQMAG
      Average0.5864.3025.7540.6304.87911.361
      10.5864.4843.1800.6104.9707.970
      20.6213.7223.7770.6714.4457.292
      30.5904.0977.7660.6014.81113.132
      40.6094.0285.4700.6234.5248.997
      50.5624.6335.9430.6374.94010.293
      60.5904.6105.8670.6304.67411.307
      70.6384.8946.9470.6424.83411.302
      80.4783.0183.5280.6105.31014.334

      9

      10

      0.616

      0.566

      4.816

      4.716

      9.540

      5.518

      0.640

      0.626

      4.642

      5.642

      15.520

      13.462

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    Xiaoqi Wang, Xuanzhi Zhao, Zengli Liu. Underwater Optical Image Enhancement Based on Color Constancy and Multiscale Wavelet[J]. Laser & Optoelectronics Progress, 2022, 59(16): 1601002

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    Paper Information

    Category: Atmospheric Optics and Oceanic Optics

    Received: Jun. 28, 2021

    Accepted: Jul. 4, 2021

    Published Online: Jul. 22, 2022

    The Author Email: Zhao Xuanzhi (1649077559@qq.com)

    DOI:10.3788/LOP202259.1601002

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