  Underwater imagery often exhibits distorted coloration as a result of
light-water interactions, which complicates the study of benthic environments
in marine biology and geography. In this research, we propose an algorithm to
restore the true color (albedo) in underwater imagery by jointly learning the
effects of the medium and neural scene representations. Our approach models
water effects as a combination of light attenuation with distance and
backscattered light. The proposed neural scene representation is based on a
neural reflectance field model, which learns albedos, normals, and volume
densities of the underwater environment. We introduce a logistic regression
model to separate water from the scene and apply distinct light physics during
training. Our method avoids the need to estimate complex backscatter effects in
water by employing several approximations, enhancing sampling efficiency and
numerical stability during training. The proposed technique integrates
underwater light effects into a volume rendering framework with end-to-end
differentiability. Experimental results on both synthetic and real-world data
demonstrate that our method effectively restores true color from underwater
imagery, outperforming existing approaches in terms of color consistency.
