Looking Into the Water by Unsupervised Learning of the Surface Shape

Cameras in the surf zone or behind ship wakes look through turbid, bubbly, moving water close to the lens, yet restoration networks are usually scored only on displayed images. We introduce a measured dataset of a telescope viewing displayed images and physical objects through a 25mm water layer 45mm in front of its objective, under seven combinations of flow, bubbles and turbidity, with clear-water recordings as a second reference. Because the suspension scatters light far outside the 1.3\textdegree{} field of view, turbidity mainly removes contrast, 4.4- to 4.8-fold on a Siemens star. Restoring the mean of eight frames improves structural similarity (SSIM) by 0.032 over a single frame, and learned fusion of the frames adds little. One raw frame supports five-category recognition (96.8%) as well as restored images. Adding synthetic training pairs leaves display-image scores unchanged but raises the correlation of a restored Siemens star with its clear-water recording from below 0.5 to above 0.9. These networks also improve ordinary underwater photographs without retraining, where training on measured pairs alone fails. Scoring against the digital source or a clear-water recording reverses the ranking of raw and restored images. These results support a simple pipeline: average the frames, classify raw frames directly, and restore with networks trained on measured and synthetic pairs.

Underwater 3D reconstruction and appearance restoration remain challenging due to the complex optical properties of water, such as wavelength-dependent attenuation and scattering. Existing Neural Radiance Fields (NeRF)-based approaches often suffer from slow rendering and limited practicality, while vanilla 3D Gaussian Splatting (3DGS) lacks an effective mechanism to account for underwater degradation. To address this, we propose WaterClear-GS, a physics-informed reformulation of underwater Gaussian splatting that models degradation as intrinsic Gaussian attributes rather than external medium fields. Furthermore, we adopt a dual-branch optimization strategy that preserves underwater photometric consistency while encouraging a practical separation between restoration-oriented latent clean appearance and degradation. This strategy is enhanced by depth-guided geometry regularization and perception-driven image supervision, together with exposure constraints, spatially adaptive regularization, and physics-informed spectral regularization, which collectively promote spatial coherence and plausible visual appearance. Extensive experiments on standard benchmarks and our collected dataset demonstrate that WaterClear-GS achieves strong performance on both novel view synthesis (NVS) and underwater image restoration (UIR) tasks, while maintaining 160+ FPS real-time rendering. The code will be available at https://sherii888.github.io/WaterClear-GS/.

Underwater image restoration consists of recovering an image which looks like there is no water present. To date, evaluation has not been systematic. This paper describes a systematic evaluation pipeline for underwater reconstruction, which can be used to assess a method for accuracy; consistency of reconstruction over camera moves; and the effect of water parameters. We use this pipeline to evaluate a range of current procedures, from models constructed using explicit but approximate physical models of scattering to Vision-Language Models (VLMs which are not currently trained with explicit physical models). Overall, VLMs wholly and significantly outperform physically based models in our evaluation, likely because of the importance of a strong image prior. Results on images of real underwater scenes strongly confirm the evaluation.