  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.
