• Laser & Optoelectronics Progress
  • Vol. 59, Issue 16, 1601002 (2022)
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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    DOI: 10.3788/LOP202259.1601002 Cite this Article Set citation alerts
    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 Copy Citation Text show less
    Flow chart of proposed algorithm
    Fig. 1. 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
    Fig. 2. 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
    Fig. 3. 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
    Fig. 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;
    Fig. 5. 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
    Fig. 6. 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
    Fig. 7. 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
    Fig. 8. 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
    Fig. 9. 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
    Fig. 10. Application test results. (a) Original image matching results; (b) enhanced image matching results
    Evaluation results of low illumination image
    Fig. 11. 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
    Fig. 12. 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
    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

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