Hybrid Underwater Image Enhancement Based on Color Transfer and Adaptive Gain Control
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摘要: 针对水下图像的颜色偏差和低对比度等退化问题, 提出了一种基于颜色转移与自适应增益控制融合的水下图像增强方法. 首先, 根据颜色转移图像和最大衰减图引导的融合策略校正水下图像的颜色偏差. 其次, 利用一阶原始对偶方法对V通道进行滤波以有效地抑制噪声的干扰, 获得结构图像; 并且提出自适应增益控制, 根据图像的高频信息自适应调整增益, 以获得细节图像. 最后, 通过加权融合结构图像与细节图像, 得到高质量的水下图像. 实验结果表明, 针对不同自然和工业环境下的水下图像, 1) 所提方法可以有效地校正颜色失真现象; 2) 显著提高水下图像的对比度并且抑制噪声干扰; 3) 在定量评价指标和高级视觉任务(目标检测、图像分割、关键点检测和水下双目测距)中优于其它主流水下图像增强方法, 为水下目标抓取等工程应用提供了有益的参考.Abstract: Aiming at handing the degradation problems of color deviation and low contrast in underwater images, an underwater image enhancement method based on the fusion of color transfer and adaptive gain control is proposed. Firstly, the color deviation of the underwater image was corrected using the color transfer image and the maximum attenuation map-guided fusion strategy. Secondly, the structure image of the V channel is obtained based on the first-order primal-dual algorithm to effectively suppress the noise interference. Furthermore, an adaptive gain control is proposed to dynamically adjust high-frequency information of the V channel to obtain detail image. Finally, the structure image and the detail image are weighted and fused to obtain a high-quality underwater image. The experimental results demonstrate that for underwater images with various natural and industrial environments, 1) the outputs of the proposed method can effectively correct the color distortion; 2) it can improve the contrast of underwater images and suppress the noise interference; 3) the quantitative metrics and high-level visual tasks (object detection, image segmentation, key-point detection and underwater binocular distance measurement) are superior to other state-of-the-art underwater enhancement methods, which provides a useful reference for engineering applications such as underwater automated grasping.
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图 6 基于颜色转移的水下颜色校正方法的增强图像
Fig. 6 Enhanced results of the underwater color correction method based on color transfer
图 7 不同主流方法在UIEB数据集上的视觉对比
Fig. 7 Visual comparisons of different mainstream methods on the UIEB dataset
图 9 不同主流方法在RISU数据集上的视觉对比
Fig. 9 Visual comparisons of different mainstream methods on the RISU dataset
图 10 不同方法在UIEB数据集上的细节对比
Fig. 10 Detailed comparison of different methods on the UIEB dataset
图 14 各组件在不同退化水下图像上的视觉消融结果
Fig. 14 Visual ablation results of each component on different degraded underwater images
图 16 不同主流方法在双目棋盘格图像上的视觉对比
Fig. 16 Visual comparison of different mainstream methods on binocular checkerboard images
表 1 基于UIQM指标的定量结果
Table 1 Quantitative results based on UIQM metrics
IBLA GDCP UNTV Sea-thru MLLE BRUIE WaterNet 本文方法 image1 3.739 3.425 3.868 4.340 4.751 5.183 4.307 4.971 image2 4.693 5.174 3.719 4.665 4.149 4.742 3.631 4.848 image3 2.451 1.223 3.696 2.876 3.545 3.995 3.057 3.834 image4 1.991 2.906 2.992 2.551 2.776 4.002 2.787 2.862 image5 2.510 1.762 5.768 4.395 4.841 5.113 4.580 5.313 image6 3.300 1.727 4.883 4.460 4.701 5.259 3.971 5.110 image7 4.052 1.781 4.862 4.333 4.756 5.248 4.078 4.902 image8 3.514 2.771 5.118 5.019 4.745 5.242 4.493 5.396 
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表 2 定量比较不同算法在目标检测上的性能
Table 2 Quantitative comparison of the performance of different mainstream methods on object detection
AP$(\%)$ $AP_{50}$$(\%)$ $AP_{75}$$(\%)$ $AR_{1}$$(\%)$ $AR_{10}$$(\%)$ $AR_{100}$$(\%)$ IBLA 0.502 0.856 0.524 0.168 0.554 0.618 GDCP 0.493 0.854 0.516 0.159 0.531 0.588 UNTV 0.509 0.873 0.513 0.168 0.552 0.606 Sea-thru 0.499 0.862 0.506 0.164 0.548 0.601 MLLE 0.509 0.871 0.530 0.161 0.550 0.599 BRUIE 0.499 0.859 0.529 0.154 0.538 0.597 WaterNet 0.501 0.862 0.515 0.162 0.532 0.591 本文方法 0.514 0.871 0.558 0.167 0.551 0.609
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表 3 各组件的定量消融结果
Table 3 Quantitative ablation results of each component
image1 image2 image3 image4 image5 无颜色校正方法 4.539 0.475 4.804 1.573 3.584 无最大衰减图 5.160 3.802 5.013 4.465 4.474 无细节增强方法 4.356 2.465 4.244 3.277 4.152 无细节图像 4.991 3.402 4.517 4.188 4.434 完整方法 5.246 4.131 5.029 4.921 4.517
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表 4 水下视觉测量
Table 4 Measurement of underwater vision
Raw IBLA GDCP UNTV Sea-thru MLLE BRUIE WaterNet 本文方法 RE (像素) 0.166 0.183 0.174 0.320 0.224 0.162 0.185 0.178 0.133 AD (mm) 28.38 28.57 28.60 26.59 28.65 28.38 28.62 28.56 28.87
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