Simulate an Underwater Imaging System and Explore Water Absorption and Scattering Estimation Methods

Introduction

Motivation: There is a growing demand for high-speed wireless underwater data transmission. Optical communication provides one method for achieving this, given its inherently large bandwidth, relatively low attenuation, and ability to form secure point-to-point connections. However, underwater wireless optical communication (UWOC) is impacted significantly by distortions due to optical turbulence, caused by small changes in the refractive index of the water due to different medium conditions.

What has been done in the past: In the past, researchers have superimposed Laguerre-Gaussian (LG) beams to create an alphabet of spatially multiplexed symbols that can be encoded with information and decoded at the receiver by a Convolutional Neural Network (CNN). LG beams can be superimposed due to their orthogonality due to the fact they carry orbital angular momentum (OAM), and they have been shown to have inherent resilience to optical turbulence. Through signal processing and machine learning methods, researchers can reduce a certain amount of impact of oceanic optical turbulence on the received signal. However, the challenges in the oceanic environment motivate investigation into understanding the water medium better to further improve optical communication.

Goal: The goal of this project is to develop experimental designs aimed at characterizing a participating medium using camera captures. The absorption and scattering properties are fundamental characteristics of a participating medium such as water. Since measuring these two properties of real water or other media is difficult, In this project, we use camera simulations and virtual 3D environment techniques to test these parameters in order to create accurate models that faithfully model light interactions with such a medium, understand how they impact a medium, and hence simulate the appearance of an image captured by an underwater camera.