  Over the past few decades, underwater image enhancement has attracted
increasing amount of research effort due to its significance in underwater
robotics and ocean engineering. Research has evolved from implementing
physics-based solutions to using very deep CNNs and GANs. However, these
state-of-art algorithms are computationally expensive and memory intensive.
This hinders their deployment on portable devices for underwater exploration
tasks. These models are trained on either synthetic or limited real world
datasets making them less practical in real-world scenarios. In this paper we
propose a shallow neural network architecture, \textbf{Shallow-UWnet} which
maintains performance and has fewer parameters than the state-of-art models. We
also demonstrated the generalization of our model by benchmarking its
performance on combination of synthetic and real-world datasets.
