  This paper reports on WaterGAN, a generative adversarial network (GAN) for
generating realistic underwater images from in-air image and depth pairings in
an unsupervised pipeline used for color correction of monocular underwater
images. Cameras onboard autonomous and remotely operated vehicles can capture
high resolution images to map the seafloor, however, underwater image formation
is subject to the complex process of light propagation through the water
column. The raw images retrieved are characteristically different than images
taken in air due to effects such as absorption and scattering, which cause
attenuation of light at different rates for different wavelengths. While this
physical process is well described theoretically, the model depends on many
parameters intrinsic to the water column as well as the objects in the scene.
These factors make recovery of these parameters difficult without simplifying
assumptions or field calibration, hence, restoration of underwater images is a
non-trivial problem. Deep learning has demonstrated great success in modeling
complex nonlinear systems but requires a large amount of training data, which
is difficult to compile in deep sea environments. Using WaterGAN, we generate a
large training dataset of paired imagery, both raw underwater and true color
in-air, as well as depth data. This data serves as input to a novel end-to-end
network for color correction of monocular underwater images. Due to the
depth-dependent water column effects inherent to underwater environments, we
show that our end-to-end network implicitly learns a coarse depth estimate of
the underwater scene from monocular underwater images. Our proposed pipeline is
validated with testing on real data collected from both a pure water tank and
from underwater surveys in field testing. Source code is made publicly
available with sample datasets and pretrained models.
