Fast Underwater Image Enhancement for Improved Visual Perception

In this letter, we present a conditional generative adversarial network-based model for real-time underwater image enhancement. To supervise the adversarial training, we formulate an objective function that evaluates the perceptual image quality based on its global content, color, local texture, and style information. We also present EUVP,a large-scale dataset of a paired and an unpaired collection of underwater images (of `poor' and `good' quality) that are captured using seven different cameras over various visibility conditions during oceanic explorations and human-robot collaborative experiments. In addition, we perform several qualitative and quantitative evaluations which suggest that the proposed model can learn to enhance underwater image quality from both paired and unpaired training. More importantly, the enhanced images provide improved performances of standard models for underwater object detection, human pose estimation, and saliency prediction. These results validate that it is suitable for real-time preprocessing in the autonomy pipeline by visually-guided underwater robots. The model and associated training pipelines are available athttps://github.com/xahidbuffon/funie-gan.

I. Introduction

Visually-guided AUVs (Autonomous Underwater Vehicles) and ROVs (Remotely Operated Vehicles) are widely used in important applications such as the monitoring of marine species migration and coral reefs [1], inspection of submarine cables and wreckage [2], underwater scene analysis, seabed mapping, human-robot collaboration [3], and more. One major operational challenge for these underwater robots is that despite using high-end cameras, visual sensing is often greatly affected by poor visibility, light refraction, absorption, and scattering [3]–[5]. These optical artifacts trigger non-linear distortions in the captured images, which severely affect the performance of vision-based tasks such as tracking, detection and classification, segmentation, and visual servoing. Fast and accurate image enhancement techniques can alleviate these problems by restoring the perceptual and statistical qualities [5], [6] of the distorted images in real-time.