  The powerful representation capacity of deep learning has made it inevitable
for the underwater image enhancement community to employ its potential. The
exploration of deep underwater image enhancement networks is increasing over
time, and hence; a comprehensive survey is the need of the hour. In this paper,
our main aim is two-fold, 1): to provide a comprehensive and in-depth survey of
the deep learning-based underwater image enhancement, which covers various
perspectives ranging from algorithms to open issues, and 2): to conduct a
qualitative and quantitative comparison of the deep algorithms on diverse
datasets to serve as a benchmark, which has been barely explored before. To be
specific, we first introduce the underwater image formation models, which are
the base of training data synthesis and design of deep networks, and also
helpful for understanding the process of underwater image degradation. Then, we
review deep underwater image enhancement algorithms, and a glimpse of some of
the aspects of the current networks is presented including network
architecture, network parameters, training data, loss function, and training
configurations. We also summarize the evaluation metrics and underwater image
datasets. Following that, a systematically experimental comparison is carried
out to analyze the robustness and effectiveness of deep algorithms. Meanwhile,
we point out the shortcomings of current benchmark datasets and evaluation
metrics. Finally, we discuss several unsolved open issues and suggest possible
research directions. We hope that all efforts done in this paper might serve as
a comprehensive reference for future research and call for the development of
deep learning-based underwater image enhancement.
