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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 lightattenuation and back-scattering result in color distortion and haze effect,which degrade the visibility of images. To address this problem, firstly, weproposed an unsupervised generative adversarial network (GAN) for generatingrealistic underwater images (color distortion and haze effect) from in-airimage and depth map pairs based on improved underwater imaging model. Secondly,U-Net, which is trained efficiently using synthetic underwater dataset, isadopted for color restoration and dehazing. Our model directly reconstructsunderwater clear images using end-to-end autoencoder networks, whilemaintaining scene content structural similarity. The results obtained by ourmethod were compared with existing methods qualitatively and quantitatively.Experimental results obtained by the proposed model demonstrate wellperformance on open real-world underwater datasets, and the processing speedcan reach up to 125FPS running on one NVIDIA 1060 GPU. Source code, sampledatasets are made publicly available athttps://github.com/infrontofme/UWGAN_UIE.

UWGAN: Underwater GAN for Real-world Underwater Color Restoration and Dehazing