A Non-Reference Evaluation of Underwater Image Enhancement Methods Using a New Underwater Image Dataset

Abstract

The rise of vision-based environmental, marine, and oceanic exploration research highlights the need for supporting underwater image enhancement techniques to help mitigate water effects on images such as blurriness, low color contrast, and poor quality. This paper presents an evaluation of common underwater image enhancement techniques using our new publicly-available Challenging Dataset for Underwater Image Enhancement (CDUIE). The collected dataset is comprised of 85 images of aquatic plants taken at a shallow depth of up to three meters from three different locations in the Great Lake Superior, USA, via a Remotely Operated Vehicle (ROV) equipped with a high-definition RGB camera. In particular, we use our dataset to benchmark nine state-of-the-art image enhancement models at three different depths using a set of common non-reference image quality evaluation metrics. Then we provide a comparative analysis of the performance of the selected models at different depths and highlight the most prevalent ones. The obtained results show that the selected image enhancement models are capable of producing considerably better-quality images with some models performing better than others at certain depths. The dataset is available at https://www.github.com/ashrafrepo/underwater-image-enhancement.

Keywords

• Underwater image dataset
• deep learning
• non-reference image quality metrics
• ROV
• underwater image enhancement