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.

翻译：水下图像常因光-水相互作用而呈现颜色失真，这给海洋生物学和地理学中底栖环境的研究带来了复杂性。在本研究中，我们提出了一种算法，通过联合学习介质效应与神经场景表示，恢复水下图像的真实颜色（反照率）。我们的方法将水效应建模为随距离变化的光衰减与后向散射光的组合。所提出的神经场景表示基于神经反射场模型，可学习水下环境的反照率、法向和体积密度。我们引入逻辑回归模型将水与场景分离，并在训练过程中应用不同的光照物理机制。通过采用多种近似方法，本方法避免了估计水下复杂后向散射效应的需求，从而提升了训练过程中的采样效率与数值稳定性。该技术将水下光照效应整合到具有端到端可微性的体渲染框架中。在合成数据与真实数据上的实验结果表明，我们的方法能有效恢复水下图像的真色，在色彩一致性方面优于现有方法。