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海底主动光学探测影像亮度校正与色彩恢复

刘镕滔 柳稼航

刘镕滔, 柳稼航. 海底主动光学探测影像亮度校正与色彩恢复[J]. 中国光学(中英文), 2022, 15(4): 689-702. doi: 10.37188/CO.2021-0211
引用本文: 刘镕滔, 柳稼航. 海底主动光学探测影像亮度校正与色彩恢复[J]. 中国光学(中英文), 2022, 15(4): 689-702. doi: 10.37188/CO.2021-0211
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

海底主动光学探测影像亮度校正与色彩恢复

doi: 10.37188/CO.2021-0211
基金项目: 高分辨率对地观测系统计划(No. 41-Y30F07-9001-20/22);江苏省SC人才项目(No. JSSCRC2021501)
详细信息
    作者简介:

    刘镕滔(1997—),男,湖南娄底人,2019年于重庆大学通信工程专业获得学士学位,现于南京航空航天大学航天学院攻读通信与信息系统专业硕士研究生,研究方向为图像复原与增强。E-mail:lrt@nuaa.edu.cn

    柳稼航(1977—),男,博士,教授,2000年于武汉大学获得摄影测量与遥感工学学士学位;2003年于中国地震局地质研究所获得构造地质理学硕士学位;2011年于上海交通大学获得模式识别与智能系统工学博士学位。2003年—2019年,在中国科学院西安光学精密机械研究所工作,历任实习研究员,助理研究员、副研究员、研究员。现为南京航空航天大学航天学院教授,博士生导师。主要从事遥感技术,图像处理,机器视觉及人工智能等方面的研究。E-mail:jhliu@nuaa.edu.cn

  • 中图分类号: TP391

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

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
  • 摘要:

    主动光学成像探测是海底形貌与环境探测的重要方式,广泛应用于大洋勘探、海底探测等领域。然而,由于海水对光的衰减作用,造成光学影像照度不均、颜色失真、对比度低等质量退化问题。本文依据水下主动光学成像探测的特点,提出了一种基于相对辐射校正原理的水下图像增强方法。该方法将增强过程分为亮度补偿和色彩恢复两个阶段。在亮度补偿阶段,依据水下点光源的成像特点和辐射衰减机制,采用相对辐射校正原理对水下图像分通道进行补偿,消除因光源不均、光程不同等因素造成的亮度畸变。在色彩恢复阶段,首先对红通道图像进行自适应补偿和色彩粗平衡,在此基础上进一步利用Retinex模型对图像进行色彩恢复。利用实际的海底勘探图像进行实验验证,结果表明本文方法的增强结果亮度均匀、色彩自然,有效提升了图像质量。相较现有方法,本文方法的结果无论主观感受还是客观评价整体更优。同时,由于本文方法不需要光源、相机等特性参数,仅利用实际观测图像本身进行校正,因而具有更好的适应性。

     

  • 图 1  点光源垂直照射下的照度相对分布示意图。(a)理想点光源垂直照射示意图;(b)光照强度衰减示意图

    Figure 1.  Relative distribution of illuminance under vertical illumination of the point light source . (a) Schematic of vertical illumination of the ideal point light source and (b) schematic of light intensity attenuation

    图 2  海底主动光学成像示意图

    Figure 2.  Schematic diagram of seabed active optical imaging

    图 3  点光源照明下的均匀海底光学图像

    Figure 3.  Underwater optical image of a uniform seafloor illuminated by the point light source

    图 4  参考图像的各通道图像及其亮度拟合曲面。(a)~(c)参考图像的红、绿、蓝通道;(d)~(f)红、绿、蓝通道的三维俯视图;(g)~(i)红、绿、蓝通道的拟合曲面

    Figure 4.  Each channel of the reference image and their brightness fitting surface (a)−(c) red, green, blue channels of the reference image; (d)−(f) 3D top view of the red, green and blue channels; (g)−(i) fitting surface of the red, green, blue channels

    图 5  参考图像分通道亮度补偿结果。(a)~(c)分别为红、绿、蓝三通道的亮度补偿量;(d)~ (f)补偿后的红、绿、蓝单通道影像

    Figure 5.  Individual brightness compensation for each channel of the reference image (a)−(c) Degree of compensation of the red, green and blue channels; (d)−(f) compensated red, green and blue single channel images

    图 6  采用参考图像校正参数对同批次的图像补偿。(a)~(c)红绿蓝三通道原始灰度图像;(d)~(f)补偿后的红绿蓝影像

    Figure 6.  The compensation process of the image in the same batch with reference image correction parameters. (a)−(c) Original gray images of the red, green and blue channels, (d)−(f) compensated images of the three channels

    图 8  不同算法对参考图像校正的结果。(a)MSR;(b)CLAHE;(c)RCP;(d)RWCGC;(e)UWCNN;(f)本文方法

    Figure 8.  Comparison of correction results of the underwater image processed by different algorithms. (a) MSR; (b) CLAHE; (c) RCP; (d) RWCGC; (e) UWCNN; (f) The proposed method

    图 9  水下图像常用增强处理算法的结果对比。(a)原始图片;(b)~(g)分别使用MSR、CLAHE、RCP、RWCGC、UWCNN和本文方法的增强结果

    Figure 9.  Comparison of underwater image enhancement processed by different algorithms. (a) Original images; (b)−(g) the results obtained by MSR, CLAHE, RCP, RWCGC, UWCNN and the proposed method

    图 7  不同图像增强方法效果对比。(a)预补偿输入图像;(b)~(f)分别是灰色世界、本文预处理方法、MSR、灰色世界算法+MSR、本文预处理方法+MSR方法结果

    Figure 7.  Comparison of image enhancement with different pre-processing methods. (a) Pre-compensation image; (b)−(f) compensated results with Gray world, the proposed pre-processing method, MSR, Gray world + MSR, and the proposed pre-processing method + MSR methods

    表  1  参考图像各通道灰度曲面四次多项式拟合参数

    Table  1.   Quartic polynomial fitting parameters of the gray surface of each channel of the reference image

    参数R通道G通道B通道
    a0019.2546.3250.18
    a10−0.10060.42080.4696
    a01−0.11520.39040.4299
    a201.005×10−3−4.915×10−4 −7.192×10−4
    a111.038×10−3−6.395×10−4−8.548×10−4
    a021.002×10−3−4.785×10−4−6.328×10−4
    a30−2.241×10−61.353×10−76.774×10−7
    a21−1.584×10−6−2.121×10−7−1.141×10−7
    a12−7.252×10−72.581×10−63.145×10−6
    a03−2.75×10−6−8.219×10−7−5.752×10−7
    a401.391×10−9−8.548×10−10−1.382×10−9
    a313.485×10−103.321×10−94.136×10−9
    a222.072×10−9−4.899×10−9−6.476×10−9
    a13−1.332×10−94.571×10−101.053×10−9
    a042.382×10−93.993×10−107.139×10−11
    下载: 导出CSV

    表  2  参考图像及其在不同算法处理后的客观评价结果

    Table  2.   Objective image quality evaluation results of the reference image and images processed by different algorithms

    指标参考图像MSRCLAHERCPRWCGCUWCNNOurs
    UIQM0.93223.8092.31933.35353.15081.42384.0546
    UCIQE20.398824.981325.617324.043328.784721.454628.2705
    注:加粗字体为每行最优值。
    下载: 导出CSV

    表  3  对于图9第一列影像不同算法的客观评价结果

    Table  3.   Objective image quality evaluation results of the first column of Fig. 9 processed by different algorithms

    指标图9第一列原图MSRCLAHERCPRWCGCUWCNNOurs
    UIQM1.01473.85572.99082.69693.64443.02114.5062
    UCIQE18.257528.071324.380222.439729.377821.901729.8468
    注:加粗字体为每行最优值。
    下载: 导出CSV

    表  4  对于图9第二列影像不同算法的客观质量评价指标

    Table  4.   Objective image quality evaluation indexes of the second column of Fig. 9 processed by different algorithms

    指标图9第二列原图MSRCLAHERCPRWCGCUWCNNOurs
    UIQM1.01473.85572.99082.69693.64443.02114.5583
    UCIQE20.556127.670927.543924.287729.077820.952329.5887
    注:加粗字体为每行最优值
    下载: 导出CSV

    表  5  对于图9第三列影像不同算法的客观质量评价指标

    Table  5.   Objective image quality evaluation indexes of the third column of Fig. 9 processed by different algorithms

    图9第三列原图MSRCLAHERCPRWCGCUWCNNOurs
    UIQM0.67393.71422.83592.46113.121462.61834.3095
    UCIQE20.031228.139225.583123.894329.099622.886829.1635
    注:加粗字体为每行最优值
    下载: 导出CSV
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  • 收稿日期:  2021-12-06
  • 修回日期:  2022-01-10
  • 网络出版日期:  2022-05-16

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