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

Understand

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.