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

DGD-cGAN: A Dual Generator for Image Dewatering and Restoration