Volume 15 Issue 4
Jul.  2022
Turn off MathJax
Article Contents
LIU Rong-tao, LIU Jia-hang. Brightness correction and color restoration of seabed image obtained by active optical detection[J]. Chinese Optics, 2022, 15(4): 689-702. doi: 10.37188/CO.2021-0211
Citation: LIU Rong-tao, LIU Jia-hang. Brightness correction and color restoration of seabed image obtained by active optical detection[J]. Chinese Optics, 2022, 15(4): 689-702. doi: 10.37188/CO.2021-0211

Brightness correction and color restoration of seabed image obtained by active optical detection

doi: 10.37188/CO.2021-0211
Funds:  Supported by the China High Resolution Earth Observation System Program (No. 41-Y30F07-9001-20/22); Innovative talent program of Jiangsu (No. JSSCRC2021501)
More Information
  • Corresponding author: jhliu@nuaa.edu.cn
  • Received Date: 06 Dec 2021
  • Rev Recd Date: 10 Jan 2022
  • Available Online: 16 May 2022
  • Active optical imaging detection is an important method for seabed topography and environment detection, which is widely used in ocean exploration. However, due to the attenuation effect of light in seawater, the optical images often suffer uneven illumination, color distortion and low contrast. According to the property of underwater active optical imaging, an underwater image enhancement method based on relative radiometric correction is proposed in this paper. The procedure is divided into brightness compensation and color restoration. In brightness compensation, according to the imaging characteristics and radiation attenuation mechanism of a point light source, the relative radiation correction is used to compensate for the channels of underwater images. This stage eliminates the brightness distortion caused by an uneven light source, varying optical paths and so on. In the color restoration, adaptive compensation and rough color balance are performed first on the red channel. Then, the Retinex model is used to restore colors. The real seabed images are used for experiments. The results show that the enhanced images by the proposed method have uniform brightness and natural look. Compared with the other methods, the results of the proposed method are better overall both subjectively and objectively. At the same time, the method proposed in this paper does not need the properties of light source, camera and others. Only the real detection images themselves are used for correction, and achieve better adaptability.


  • loading
  • [1]
    邓翔宇, 王惠刚, 张永庆. 基于主动光照的深海图像增强算法[J]. 光子学报,2020,49(3):0310001. doi: 10.3788/gzxb20204903.0310001

    DENG X Y, WANG H G, ZHANG Y Q. Deep sea image enhancement method based on the active illumination[J]. Acta Photonica Sinica, 2020, 49(3): 0310001. (in Chinese) doi: 10.3788/gzxb20204903.0310001
    郭继昌, 李重仪, 郭春乐, 等. 水下图像增强和复原方法研究进展[J]. 中国图象图形学报,2017,22(3):273-287.

    GUO J CH, LI CH Y, GUO CH L, et al. Research progress of underwater image enhancement and restoration methods[J]. Journal of Image and Graphics, 2017, 22(3): 273-287. (in Chinese)
    HENKE B, VAHL M, ZHOU ZH L. Removing color cast of underwater images through non-constant color constancy hypothesis[C]. 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA), IEEE, 2013.
    FU X Y, ZHUANG P X, HUANG Y, et al.. A retinex-based enhancing approach for single underwater image[C]. 2014 IEEE International Conference on Image Processing (ICIP), IEEE, 2014.
    杨卫中, 徐银丽, 乔曦, 等. 基于对比度受限直方图均衡化的水下海参图像增强方法[J]. 农业工程学报,2016,32(6):197-203. doi: 10.11975/j.issn.1002-6819.2016.06.027

    YANG W ZH, XU Y L, QIAO X, et al. Method for image intensification of underwater sea cucumber based on contrast-limited adaptive histogram equalization[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(6): 197-203. (in Chinese) doi: 10.11975/j.issn.1002-6819.2016.06.027
    XIANG W D, YANG P, WANG SH, et al. Underwater image enhancement based on red channel weighted compensation and gamma correction model[J]. Opto-Electronic Advances, 2018, 1(10): 180024.
    HE K M, SUN J, TANG X O. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12): 2341-2353. doi: 10.1109/TPAMI.2010.168
    CHIANG J Y, CHEN Y C. Underwater image enhancement by wavelength compensation and dehazing[J]. IEEE Transactions on Image Processing, 2012, 21(4): 1756-1769. doi: 10.1109/TIP.2011.2179666
    GALDRAN A, PARDO D, PICÓN A, et al. Automatic red-channel underwater image restoration[J]. Journal of Visual Communication and Image Representation, 2015, 26: 132-145. doi: 10.1016/j.jvcir.2014.11.006
    谢昊伶, 彭国华, 王凡, 等. 基于背景光估计与暗通道先验的水下图像复原[J]. 光学学报,2018,38(1):0101002. doi: 10.3788/AOS201838.0101002

    XIE H L, PENG G H, WANG F, et al. Underwater image restoration based on background light estimation and dark channel prior[J]. Acta Optica Sinica, 2018, 38(1): 0101002. (in Chinese) doi: 10.3788/AOS201838.0101002
    王一斌, 尹诗白, 吕卓纹. 自适应背景光估计与非局部先验的水下图像复原[J]. 光学 精密工程,2019,27(2):499-510. doi: 10.3788/OPE.20192702.0499

    WANG Y B, YIN SH B, LÜ ZH W. Underwater image restoration with adaptive background light estimation and non-local prior[J]. Optics and Precision Engineering, 2019, 27(2): 499-510. (in Chinese) doi: 10.3788/OPE.20192702.0499
    林森, 刘世本, 唐延东. 多输入融合对抗网络的水下图像增强[J]. 红外与激光工程,2020,49(5):20200015. doi: 10.3788/irla.28_2020-0015

    LIN S, LIU SH B, TANG Y D. Multi-input fusion adversarial network for underwater image enhancement[J]. Infrared and Laser Engineering, 2020, 49(5): 20200015. (in Chinese) doi: 10.3788/irla.28_2020-0015
    LI CH Y, ANWAR S, PORIKLI F. Underwater scene prior inspired deep underwater image and video enhancement[J]. Pattern Recognition, 2020, 98: 107038. doi: 10.1016/j.patcog.2019.107038
    刘群, 刘崇, 朱小磊, 等. 星载海洋激光雷达最佳工作波长分析[J]. 中国光学,2020,13(1):148-155. doi: 10.3788/co.20201301.0148

    LIU Q, LIU CH, ZHU X L, et al. Analysis of the optimal operating wavelength of spaceborne oceanic lidar[J]. Chinese Optics, 2020, 13(1): 148-155. (in Chinese) doi: 10.3788/co.20201301.0148
    全向前, 陈祥子, 全永前, 等. 深海光学照明与成像系统分析及进展[J]. 中国光学,2018,11(2):153-165. doi: 10.3788/co.20181102.0153

    QUAN X Q, CHEN X Z, QUAN Y Q, et al. Analysis and research progress of deep-sea optical illumination and imaging system[J]. Chinese Optics, 2018, 11(2): 153-165. (in Chinese) doi: 10.3788/co.20181102.0153
    吕宝林, 佟首峰, 徐伟, 等. 基于配准的机载红外非均匀性校正技术应用[J]. 中国光学,2020,13(5):1124-1137. doi: 10.37188/CO.2020-0109

    LV B L, TONG SH F, XU W, et al. Non-uniformity correction of airborne infrared detection system based on inter-frame registration[J]. Chinese Optics, 2020, 13(5): 1124-1137. (in Chinese) doi: 10.37188/CO.2020-0109
    ANCUTI C O, ANCUTI C, DE VLEESCHOUWER C, et al. Color balance and fusion for underwater image enhancement[J]. IEEE Transactions on Image Processing, 2018, 27(1): 379-393. doi: 10.1109/TIP.2017.2759252
    LAND E H, MCCANN J J. Lightness and retinex theory[J]. Journal of the Optical Society of America, 1971, 61(1): 1-11. doi: 10.1364/JOSA.61.000001
    RAHMAN Z, JOBSON D J, WOODELL G A. Multi-scale retinex for color image enhancement[C]. Proceedings of 3rd IEEE International Conference on Image Processing, IEEE, 1996.
    PANETTA K, GAO CH, AGAIAN S. Human-visual-system-inspired underwater image quality measures[J]. IEEE Journal of Oceanic Engineering, 2016, 41(3): 541-551. doi: 10.1109/JOE.2015.2469915
    YANG M, SOWMYA A. An underwater color image quality evaluation metric[J]. IEEE Transactions on Image Processing, 2015, 24(12): 6062-6071. doi: 10.1109/TIP.2015.2491020
  • 加载中


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(9)  / Tables(5)

    Article views(263) PDF downloads(108) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint