Scattered Light Compensation Combined with Color Preservation and Contrast Balance for Underwater Image Enhancement
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摘要: 针对水下图像存在颜色偏差、对比度低及模糊等问题, 提出散射光补偿结合色彩保持与对比度均衡的水下图像增强方法。首先, 利用相对总变分模型分离图像结构层和纹理层, 其中, 设计基于RGB空间映射的补偿系数误差矩阵校正结构层色偏, 并通过滤波分离与融合增强纹理层, 以防止图像的初始特征丢失, 增强后的纹理层与结构层叠加得到第1层输出; 然后, 在对比度均衡模块中, 基于空间变换进行保色对比度限制自适应直方图均衡化, 进一步提高对比度和亮度; 最后, 将双层增强结构的结果图融合得到输出图像。通过在不同数据集上与其他算法进行对比, 验证文中方法在平衡色差, 增强细节和去模糊等方面均具有更好效果, 对水下无人系统视觉任务具有实际应用价值。Abstract: In view of color deviation, low contrast, and blurring in underwater images, an underwater image enhancement method based on scattered light compensation combined with color preservation and contrast balance was proposed. Firstly, the relative total variational model was used to separate the structure and texture layer of the image. Specifically, the color deviation of the structural layer was corrected by defining a compensation coefficient error matrix based on the RGB spatial mapping, and the texture layer was enhanced by filtering separation and fusion to prevent the initial feature loss of the image. The enhanced texture layer was superimposed with the structural layer to obtain the output of the first layer. Besides, in the contrast balance module, color preservation-contrast limiting adaptive histogram equalization based on spatial transformation was performed to further improve the contrast and brightness. Finally, the enhanced results of the two layers were fused to output the image. Comparison conducted on different datasets verifies that the proposed method has better performance in balancing color deviation, enhancing details, and deblurring, which has practical application value in unmanned undersea system-based vision tasks.
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表 1 CIEDE2000评价结果
Table 1. Evaluation results for CIEDE2000
方法 D10 Z33 T6000 T8000 TS1 W60 W80 平均值 原始图像 22.189 23.262 23.277 29.081 24.341 22.070 26.172 24.342 UDCP 24.407 25.075 22.401 30.886 24.644 21.167 27.934 25.216 Fusion 19.631 18.947 16.458 19.517 19.342 20.186 24.544 19.804 GDCP 25.489 24.125 22.780 22.906 21.141 23.036 30.847 24.332 Hybrid 22.421 23.031 20.938 22.994 20.508 20.815 22.785 21.927 MLLE 20.604 26.136 27.543 19.048 18.000 22.729 21.578 22.234 CFA&MR 21.217 29.297 21.544 23.643 17.034 20.982 23.915 22.519 UIESS 19.405 21.309 17.692 23.545 21.461 17.672 19.633 20.102 Shallow-UWnet 20.251 18.857 17.152 27.448 27.622 19.783 25.059 22.310 文中方法 19.339 17.913 18.235 18.814 15.663 19.066 20.815 18.549 
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表 2 UIEB和EUVP数据集评价结果
Table 2. Evaluation results on UIEB and EUVP databases
方法 UIEB EUVP SSIM PSNR FDUM NIQE SSIM PSNR FDUM NIQE UDCP 0.682 17.065 0.755 2.887 0.631 15.630 0.578 3.637 GDCP 0.599 13.525 0.854 2.921 0.597 14.306 0.682 3.470 Fusion 0.768 20.760 0.669 2.781 0.708 19.419 0.495 3.516 Hybrid 0.641 17.309 1.051 3.245 0.583 16.636 0.790 2.845 MLLE 0.692 16.111 0.948 3.523 0.631 15.462 0.702 2.940 CFA&MR 0.574 18.479 0.495 2.920 0.466 17.613 0.340 3.846 UIESS 0.624 17.811 0.585 4.034 0.687 19.898 0.537 3.985 Shallow-UWnet 0.534 16.625 0.455 3.726 0.760 20.238 0.459 4.234 文中方法 0.789 20.406 0.951 2.832 0.768 20.255 0.820 3.590
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表 3 平均运行时间
Table 3. Average running time
方法 UDCP Fusion GDCP Hybrid MLLE CFA&MR UIESS Shallow-UWnet 文中方法 时间/s 0.618 2.602 0.382 10.522 0.082 1.581 0.650 0.110 0.365
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表 4 特征点匹配结果
Table 4. Feature point matching results
方法 UDCP Fusion GDCP Hybrid MLLE CFA&MR UIESS Shallow-UWnet 文中方法 数量 21 29 37 39 23 41 37 37 44
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