  Underwater images are usually covered with a blue-greenish colour cast,
making them distorted, blurry or low in contrast. This phenomenon occurs due to
the light attenuation given by the scattering and absorption in the water
column. In this paper, we present an image enhancement approach for dewatering
which employs a conditional generative adversarial network (cGAN) with two
generators. Our Dual Generator Dewatering cGAN (DGD-cGAN) removes the haze and
colour cast induced by the water column and restores the true colours of
underwater scenes whereby the effects of various attenuation and scattering
phenomena that occur in underwater images are tackled by the two generators.
The first generator takes at input the underwater image and predicts the
dewatered scene, while the second generator learns the underwater image
formation process by implementing a custom loss function based upon the
transmission and the veiling light components of the image formation model. Our
experiments show that DGD-cGAN consistently delivers a margin of improvement as
compared with the state-of-the-art methods on several widely available
datasets.
