The majority of Earth's surface lies deep in the oceans, where no surface light reaches. Robots diving down to great depths must bring light sources that create moving illumination patterns in the darkness, such that the same 3D point appears with different color in each image. On top, scattering and attenuation of light in the water makes images appear foggy and typically blueish, the degradation depending on each pixel's distance to its observed seafloor patch, on the local composition of the water and the relative poses and cones of the light sources. Consequently, visual mapping, including image matching and surface albedo estimation, severely suffers from the effects that co-moving light sources produce, and larger mosaic maps from photos are often dominated by lighting effects that obscure the actual seafloor structure. In this contribution a practical approach to estimating and compensating these lighting effects on predominantly homogeneous, flat seafloor regions, as can be found in the Abyssal plains of our oceans, is presented. The method is essentially parameter-free and intended as a preprocessing step to facilitate visual mapping, but already produces convincing lighting artefact compensation up to a global white balance factor. It does not require to be trained beforehand on huge sets of annotated images, which are not available for the deep sea. Rather, we motivate our work by physical models of light propagation, perform robust statistics-based estimates of additive and multiplicative nuisances that avoid explicit parameters for light, camera, water or scene, discuss the breakdown point of the algorithms and show results on imagery captured by robots in several kilometer water depth.

翻译：地球表面大部分地表位于海洋深处,没有地表光。 因此, 潜水到深处的机器人必须带来光源, 从而在黑暗中创造移动的照明模式, 以至于相同的三维点在每张图像中呈现出不同的颜色。 在水中, 光线的散落和衰减使图像显得雾化, 并且通常是蓝色的, 取决于每只像素距离所观测到的海底的距离, 取决于水的当地构成以及相对成份和光源的锥体。 因此, 视觉绘图, 包括图像匹配和表面反贝多深度估计, 严重地受到共同移动的光源所产生的效果的影响, 以及照片中更大的马赛克地图往往受到光亮效应的影响, 模糊了实际海底结构。 在估算和补偿这些光亮度影响时, 正如在海洋的阿比斯平原平原平原所发现的那样, 方法基本上没有参数, 是作为预处理步骤, 用来促进视觉制图, 但是已经产生了令人信服的光深的光度值值, 从照片来源中得出了更深层的图像, 的光度的精确的精确的精确度是,, 我们不需要进行着的精确的精确的统计, 。