  Autonomous underwater vehicles (AUVs) rely on a variety of sensors -
acoustic, inertial and visual - for intelligent decision making. Due to its
non-intrusive, passive nature, and high information content, vision is an
attractive sensing modality, particularly at shallower depths. However, factors
such as light refraction and absorption, suspended particles in the water, and
color distortion affect the quality of visual data, resulting in noisy and
distorted images. AUVs that rely on visual sensing thus face difficult
challenges, and consequently exhibit poor performance on vision-driven tasks.
This paper proposes a method to improve the quality of visual underwater scenes
using Generative Adversarial Networks (GANs), with the goal of improving input
to vision-driven behaviors further down the autonomy pipeline. Furthermore, we
show how recently proposed methods are able to generate a dataset for the
purpose of such underwater image restoration. For any visually-guided
underwater robots, this improvement can result in increased safety and
reliability through robust visual perception. To that effect, we present
quantitative and qualitative data which demonstrates that images corrected
through the proposed approach generate more visually appealing images, and also
provide increased accuracy for a diver tracking algorithm.
