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摘要:
针对水下视觉同步定位与地图构建(SLAM)前端特征提取与特征匹配效果差的问题，提出一种用于水下视觉SLAM前端的基于加权融合的图像增强算法。该算法建立在2个图像的融合处理基础上，第1个图像经过基于自适应伽马校正和动态范围拉伸的水下图像亮度增强处理，第2个图像经过基于颜色判断和颜色补偿的灰度世界白平衡处理；通过计算2个图像的显著性权重和饱和性权重，对输入图像进行线性加权融合，得到最终的增强图像。通过水下彩色图像质量评价(UCIQE)和水下图像质量测量(UIQM)方法评估无参考水下增强图像质量，并用南卡罗来纳大学开源数据集测试所提算法的应用效果，结果表明：处理后，水下图像质量高、提取特征点数目多，可以显著提高水下视觉SLAM前端特征提取与特征匹配的效果。
Abstract:Aiming at the problem of poor feature extraction and feature matching in the underwater visual simultaneous localization and mapping (SLAM) front end, an image-enhanced algorithm based on weighted fusion is proposed for the underwater visual SLAM front end. Specifically, the algorithm is based on the fusion of two images: the second image is a white balance of gray world based on color judgment and color compensation, and the first image is an underwater image from brightness enhancement based on adaptive gamma correction and dynamic range expansion. Furthermore, the saliency weight and saturation weight of the two images are calculated, and the input images are linearly weighted and fused to obtain the final enhanced image.The system is tested using an open-source dataset from the University of South Carolina, and the improved underwater image quality is assessed using the techniques of underwater color image quality evaluation (UCIQE) and underwater image quality measurement (UIQM). Consequently, the results show that the processed image has the characteristics of high quality and a large number of extracted feature points, which can significantly improve the effect of front end feature extraction and feature matching of underwater visual SLAM.
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图 11 直方图均衡化处理后水下图像SIFT特征匹配
Figure 11. Feature matching by SIFT of underwater images after histogram equalization
图 12 本文算法处理后水下图像SIFT特征匹配
Figure 12. Feature matching by SIFT of underwater images after the proposed algorithm
表 1 UCIQE无参考水下图像质量评估结果
Table 1. Assessment results of no-reference underwater image quality with UCIQE
图像来源 水下图像1 水下图像2 水下图像3 水下图像4 原始水下图像 3.3788 3.2122 5.4047 4.8687 直方图均衡化处理 4.0025 4.2486 5.0472 5.1449 本文算法 5.3088 5.0171 6.5764 6.1352 
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表 2 UIQM无参考水下图像质量评估结果
Table 2. Assessment results of no-referenced underwater image quality with UIQM
图像来源 水下图像1 水下图像2 水下图像3 水下图像4 原始水下图像 0.0498 0.0698 0.2338 0.2118 直方图均衡化处理 0.4852 0.4524 0.6566 0.5556 本文算法 0.9575 1.0694 1.0917 1.0105
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表 3 水下图像特征提取数
Table 3. Number of extracted feature points for underwater images
图像来源 水下图像1 水下图像2 水下图像3 水下图像4 原始水下图像 20 93 263 326 直方图均衡化处理 306 467 729 554 本文算法 620 780 4077 3560
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表 4 水下图像正确的特征匹配对数
Table 4. Number of correct feature matching pairs for underwater images
图像来源 水下图像1、2 水下图像3、4 原始水下图像 7 15 直方图均衡化处理 39 17 本文算法 71 60
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