  Mapping the seafloor with underwater imaging cameras is of significant
importance for various applications including marine engineering, geology,
geomorphology, archaeology and biology. For shallow waters, among the
underwater imaging challenges, caustics i.e., the complex physical phenomena
resulting from the projection of light rays being refracted by the wavy
surface, is likely the most crucial one. Caustics is the main factor during
underwater imaging campaigns that massively degrade image quality and affect
severely any 2D mosaicking or 3D reconstruction of the seabed. In this work, we
propose a novel method for correcting the radiometric effects of caustics on
shallow underwater imagery. Contrary to the state-of-the-art, the developed
method can handle seabed and riverbed of any anaglyph, correcting the images
using real pixel information, thus, improving image matching and 3D
reconstruction processes. In particular, the developed method employs deep
learning architectures in order to classify image pixels to "non-caustics" and
"caustics". Then, exploits the 3D geometry of the scene to achieve a pixel-wise
correction, by transferring appropriate color values between the overlapping
underwater images. Moreover, to fill the current gap, we have collected,
annotated and structured a real-world caustic dataset, namely R-CAUSTIC, which
is openly available. Overall, based on the experimental results and validation
the developed methodology is quite promising in both detecting caustics and
reconstructing their intensity.
