An Experimental-Based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging

Wang, Yan; Song, Wei; Fortino, Giancarlo; Qi, Li-Zhe; Zhang, Wenqiang; Liotta, Antonio

Abstract

Underwater images play a key role in ocean exploration, but often suffer from severe quality degradation due to light absorption and scattering in water medium. Although major breakthroughs have been made recently in the general area of image enhancement and restoration, the applicability of new methods for improving the quality of underwater images has not specifically been captured. In this paper, we review the image enhancement and restoration methods that tackle typical underwater image impairments, including some extreme degradations and distortions. Firstly, we introduce the key causes of quality reduction in underwater images, in terms of the underwater image formation model (IFM). Then, we review underwater restoration methods, considering both the IFM-free and the IFM-based approaches. Next, we present an experimental-based comparative evaluation of state-of-the-art IFM-free and IFM-based methods, considering also the prior-based parameter estimation algorithms of the IFM-based methods, using both subjective and objective analysis (the used code is freely available at https://github.com/wangyanckxx/Single-Underwater-Image-Enhancement-and-Color-Restoration). Starting from this study, we pinpoint the key shortcomings of existing methods, drawing recommendations for future research in this area. Our review of underwater image enhancement and restoration provides researchers with the necessary background to appreciate challenges and opportunities in this important field.

Citation

Wang, Y., Song, W., Fortino, G., Qi, L.-Z., Zhang, W., & Liotta, A. (2019). An Experimental-Based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging. IEEE Access, 7, 140233-140251. https://doi.org/10.1109/ACCESS.2019.2932130

• Journal Article Type: Review
• Acceptance Date: Jul 25, 2019
• Online Publication Date: Jul 30, 2019
• Publication Date: Oct 8, 2019
• Deposit Date: Aug 2, 2019
• Publicly Available Date: Oct 7, 2019
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Pages: 140233-140251
• DOI: https://doi.org/10.1109/ACCESS.2019.2932130
• Keywords: Underwater image formation model, single underwater image enhancement, single underwater image restoration, background light estimation, transmission map estimation
• Public URL: http://researchrepository.napier.ac.uk/Output/2022902
• Contract Date: Aug 2, 2019