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Colour reconstruction of underwater images

机译：水下图像的色彩重建

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摘要

Objects look very different in the underwater environment compared to their appearance in sunlight. Images with correct colouring simplify the detection of underwater objects and may permit the use of visual simultaneous localisation and mapping (SLAM) algorithms developed for land-based robots underwater. Hence, image processing is required. Current algorithms focus on the colour reconstruction of scenery at diving depth where different colours can still be distinguished, but this is not possible at greater depth. This study investigates whether machine learning can be used to transform image data. First, laboratory tests are performed using a special light source imitating underwater lighting conditions, showing that the k-nearest neighbour method and support vector machines yield excellent results. Based on these results, an experimental verification is performed under severe conditions in the murky water of a diving basin. It shows that the k-nearest neighbour method gives very good results for short distances between the object and the camera, as well as for small water depths in the red channel. For longer distances, deeper water and the other colour channels, support vector machines are the best choice for the reconstruction of the colour as seen under white light from the underwater images.
机译：与在阳光下的外观相比，它们在水下环境中的外观差异很大。具有正确着色的图像可以简化水下物体的检测，并且可以允许使用为水下陆地机器人开发的视觉同时定位和制图（SLAM）算法。因此，需要图像处理。当前的算法着重于在潜水深度处风景的色彩重构，在该深度处仍可以区分不同的颜色，但是在更大深度处这是不可能的。这项研究调查了机器学习是否可以用于转换图像数据。首先，使用模仿水下照明条件的特殊光源进行实验室测试，结果表明，k近邻法和支持向量机可产生出色的结果。基于这些结果，在恶劣条件下在潜水盆的浑浊水中进行了实验验证。它表明，k近邻法对于物体与摄像机之间的短距离以及红色通道中的小水深都给出了很好的结果。对于更长的距离，更深的水域和其他颜色通道，支持向量机是从水下图像在白光下看到的颜色重建的最佳选择。

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