  In real-world underwater environment, exploration of seabed resources,
underwater archaeology, and underwater fishing rely on a variety of sensors,
vision sensor is the most important one due to its high information content,
non-intrusive, and passive nature. However, wavelength-dependent light
attenuation and back-scattering result in color distortion and haze effect,
which degrade the visibility of images. To address this problem, firstly, we
proposed an unsupervised generative adversarial network (GAN) for generating
realistic underwater images (color distortion and haze effect) from in-air
image and depth map pairs based on improved underwater imaging model. Secondly,
U-Net, which is trained efficiently using synthetic underwater dataset, is
adopted for color restoration and dehazing. Our model directly reconstructs
underwater clear images using end-to-end autoencoder networks, while
maintaining scene content structural similarity. The results obtained by our
method were compared with existing methods qualitatively and quantitatively.
Experimental results obtained by the proposed model demonstrate well
performance on open real-world underwater datasets, and the processing speed
can reach up to 125FPS running on one NVIDIA 1060 GPU. Source code, sample
datasets are made publicly available at
https://github.com/infrontofme/UWGAN_UIE.
