Shallow-UWnet : Compressed Model for Underwater Image Enhancement
This work is significant for underwater robotics and ocean engineering, as it enables the deployment of image enhancement models on portable devices for real-world underwater exploration tasks.
This paper addresses the computational expense and memory intensity of existing underwater image enhancement models, which limits their deployment on portable devices. The authors propose Shallow-UWnet, a shallow neural network architecture that achieves comparable performance to state-of-the-art models while using fewer parameters.
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.