  In this paper, we present a conditional generative adversarial network-based
model for real-time underwater image enhancement. To supervise the adversarial
training, we formulate an objective function that evaluates the perceptual
image quality based on its global content, color, local texture, and style
information. We also present EUVP, a large-scale dataset of a paired and
unpaired collection of underwater images (of `poor' and `good' quality) that
are captured using seven different cameras over various visibility conditions
during oceanic explorations and human-robot collaborative experiments. In
addition, we perform several qualitative and quantitative evaluations which
suggest that the proposed model can learn to enhance underwater image quality
from both paired and unpaired training. More importantly, the enhanced images
provide improved performances of standard models for underwater object
detection, human pose estimation, and saliency prediction. These results
validate that it is suitable for real-time preprocessing in the autonomy
pipeline by visually-guided underwater robots. The model and associated
training pipelines are available at https://github.com/xahidbuffon/funie-gan.
