  Neural Radiance Field (NeRF) technology demonstrates immense potential in
novel viewpoint synthesis tasks, due to its physics-based volumetric rendering
process, which is particularly promising in underwater scenes. Addressing the
limitations of existing underwater NeRF methods in handling light attenuation
caused by the water medium and the lack of real Ground Truth (GT) supervision,
this study proposes WaterHE-NeRF. We develop a new water-ray tracing field by
Retinex theory that precisely encodes color, density, and illuminance
attenuation in three-dimensional space. WaterHE-NeRF, through its illuminance
attenuation mechanism, generates both degraded and clear multi-view images and
optimizes image restoration by combining reconstruction loss with Wasserstein
distance. Additionally, the use of histogram equalization (HE) as pseudo-GT
enhances the network's accuracy in preserving original details and color
distribution. Extensive experiments on real underwater datasets and synthetic
datasets validate the effectiveness of WaterHE-NeRF. Our code will be made
publicly available.
