Osmosis: RGBD Diffusion Prior for Underwater Image Restoration
Opher Bar Nathan Deborah Levy Tali Treibitz Dan Rosenbaum
Underwater image restoration is a challenging task because of strong 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. To
overcome this critical issue, we show how to leverage in-air images to train
diffusion priors for underwater restoration. We also observe that only color
data is insufficient, and 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. Data, models and code are published in
the project page.