A paper authored by three Department of Applied Computing faculty members and their students has been published in the journal, IEEE Access.
The title of the article is, “A Non-Reference Evaluation of Underwater Image Enhancement Methods Using a New Underwater Image Dataset.” Link to the article here.
The authors of the paper are assistant professors Ashraf Saleem, Sidike Paheding, and Nathir Nathir Rawashdeh. Students Ali Awad (Applied Computing) and Navjot Kaur (Computer Science) also contributed.
Saleem is a member of the Institute of Computing and Cybersystems’s (ICC) Center for Cyber-Physical Systems (CPS); Paheding and Rawashdeh are members of the ICC’s Center for Data Sciences (DataS).
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 a new underwater image dataset. The collected dataset is comprised of 100 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.
Citation: A. Saleem, S. Paheding, N. Rawashdeh, A. Awad and N. Kaur, “A Non-Reference Evaluation of Underwater Image Enhancement Methods Using a New Underwater Image Dataset,” in IEEE Access, doi: 10.1109/ACCESS.2023.3240648.
Published in: IEEE Access ( Early Access )
Page(s): 1 – 1
Date of Publication: 30 January 2023
Electronic ISSN: 2169-3536