  Underwater image restoration is a challenging task because of water effects
that increase dramatically with distance. This is worsened by lack of ground
truth data of clean scenes without water. Diffusion priors have emerged as
strong image restoration priors. However, they are often trained with a dataset
of the desired restored output, which is not available in our case. We also
observe that using only color data is insufficient, and therefore augment the
prior with a depth channel. We train an unconditional diffusion model prior on
the joint space of color and depth, using standard RGBD datasets of natural
outdoor scenes in air. Using this prior together with a novel guidance method
based on the underwater image formation model, we generate posterior samples of
clean images, removing the water effects. Even though our prior did not see any
underwater images during training, our method outperforms state-of-the-art
baselines for image restoration on very challenging scenes. Our code, models
and data are available on the project website.
