U2PNet: An Unsupervised Underwater Image-Restoration Network Using Polarization

This article presents U
²
PNet, a novel unsupervised underwater image restoration network using polarization for improving signal-to-noise ratio and image quality in underwater imaging environments. Traditional methods for underwater image restoration using polarization require specific cues or pairs of underwater polarization datasets, which limit their practical applications. Our proposed method requires only one mosaicked polarized image of the scene and does not require datasets for pretraining or specific cues. We design two subnetworks (T-net and B
$\infty$
-net) to accurately estimate the transmission map and background light, and unique nonreference loss functions to ensure effective restoration. Our experiments are based on an indoor polarization simulated dataset and a real polarization image dataset constructed from our underwater robotic platform equipped with polarization cameras. Experiment results demonstrate that our proposed method achieves state-of-the-art performance on both simulated and real underwater polarization images. The code and datasets will be available at https://github.com/polwork/U-2Pnet.