Recovery of Underwater Visibility and Structure by Polarization Analysis

TLDR

Underwater imaging is important for scientific research, technology, and recreational activities, yet it suffers from poor visibility conditions. This study presents a computer‑vision approach to remove degradation effects in underwater images and examines the noise sensitivity of the recovery. The authors analyze the physical causes of visibility degradation, then invert the image‑formation process using pairs of images captured through a polarizer at different orientations, and evaluate the algorithm’s noise robustness. The main degradation effects are linked to partial polarization of light, the algorithm also produces a distance map, and sea experiments demonstrate nearly doubled visibility range with improved contrast and color correction.

Abstract

Underwater imaging is important for scientific research and technology as well as for popular activities, yet it is plagued by poor visibility conditions. In this paper, we present a computer vision approach that removes degradation effects in underwater vision. We analyze the physical effects of visibility degradation. It is shown that the main degradation effects can be associated with partial polarization of light. Then, an algorithm is presented, which inverts the image formation process for recovering good visibility in images of scenes. The algorithm is based on a couple of images taken through a polarizer at different orientations. As a by-product, a distance map of the scene is also derived. In addition, this paper analyzes the noise sensitivity of the recovery. We successfully demonstrated our approach in experiments conducted in the sea. Great improvements of scene contrast and color correction were obtained, nearly doubling the underwater visibility range.