# An Underwater Image Enhancement Benchmark Dataset and Beyond

## TL;DR

This paper introduces a large-scale real-world underwater image dataset and conducts a comprehensive evaluation of existing enhancement algorithms, proposing a new CNN-based method to improve underwater image quality.

## Contribution

It presents the first extensive real-world underwater image dataset (UIEB), along with a baseline enhancement network (Water-Net) trained on this dataset.

## Key findings

- Benchmark evaluations reveal strengths and limitations of current algorithms.
- Water-Net demonstrates competitive performance and generalization capabilities.
- The dataset facilitates future research in underwater image enhancement.

## Abstract

Underwater image enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real-world images. It is thus unclear how these algorithms would perform on images acquired in the wild and how we could gauge the progress in the field. To bridge this gap, we present the first comprehensive perceptual study and analysis of underwater image enhancement using large-scale real-world images. In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 real-world underwater images, 890 of which have the corresponding reference images. We treat the rest 60 underwater images which cannot obtain satisfactory reference images as challenging data. Using this dataset, we conduct a comprehensive study of the state-of-the-art underwater image enhancement algorithms qualitatively and quantitatively. In addition, we propose an underwater image enhancement network (called Water-Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs). The benchmark evaluations and the proposed Water-Net demonstrate the performance and limitations of state-of-the-art algorithms, which shed light on future research in underwater image enhancement. The dataset and code are available at https://li-chongyi.github.io/proj_benchmark.html.