Underwater LiDAR Image Enhancement Using a GAN Based Machine Learning Technique

Abstract

Robust underwater imaging sensors are essential in applications such as identifying and classifying marine animals in turbid waters, typically found around coastal monitoring sites. The Unobtrusive Multi-static Serial LiDAR Imager (UMSLI) has been developed to capture images taken in degraded underwater environments and provides superior range and image contrast over conventional optical cameras. In contrast to traditional underwater LiDAR systems that employ blue-green lasers, UMLSI will not harm the vision of marine animals, which is an important aspect in such settings. As with any other underwater LiDAR sensors, improving the quality of images taken in highly turbid water is essential to the UMSLI system. This paper proposes a novel machine learning image enhancement technique based on a Generative Adversarial Network (GAN) framework. One main contribution of the method is the incorporation of a correntropy-based perceptual loss. This technique has shown to be effective in enhancing ground-based images and has been adapted for LiDAR image enhancement. Given the limitation of existing underwater LiDAR data, a method for simulating degraded data for training is also presented. This LiDAR technique was validated using LiDAR data captured by the UMSLI system within Florida Atlantic University’s Harbor Branch Oceanographic Institute (FAU-HBOI) optical test facility and at the Marine and Coastal Research Laboratory at the Pacific Northwest National Laboratory (MCRL-PNNL). This image enhancement technique can be readily extended to other underwater LiDAR systems.