# Underwater Color Restoration Using U-Net Denoising Autoencoder

## TL;DR

This paper introduces a U-Net based denoising autoencoder for real-time underwater color restoration, significantly improving visual quality for underwater vehicle perception with a novel single autoencoder approach.

## Contribution

It presents the first autoencoder model capable of effective underwater color restoration, balancing accuracy and computational efficiency for real-time applications.

## Key findings

- Outperforms state-of-the-art methods in color restoration quality
- Enables real-time processing suitable for underwater vehicles
- Uses a novel training dataset construction method

## Abstract

Visual inspection of underwater structures by vehicles, e.g. remotely operated vehicles (ROVs), plays an important role in scientific, military, and commercial sectors. However, the automatic extraction of information using software tools is hindered by the characteristics of water which degrade the quality of captured videos. As a contribution for restoring the color of underwater images, Underwater Denoising Autoencoder (UDAE) model is developed using a denoising autoencoder with U-Net architecture. The proposed network takes into consideration the accuracy and the computation cost to enable real-time implementation on underwater visual tasks using end-to-end autoencoder network. Underwater vehicles perception is improved by reconstructing captured frames; hence obtaining better performance in underwater tasks. Related learning methods use generative adversarial networks (GANs) to generate color corrected underwater images, and to our knowledge this paper is the first to deal with a single autoencoder capable of producing same or better results. Moreover, image pairs are constructed for training the proposed network, where it is hard to obtain such dataset from underwater scenery. At the end, the proposed model is compared to a state-of-the-art method.