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.
UIEB includes two subsets: 890 raw underwater images with corresponding high-quality reference images; 60 challenging underwater images. Please share your results on this dataset with us. We will periodically update the results for noticeable underwater image enhancement methods. The UIEBD dataset is for non-commercial use only.
[Raws][Google Drive Link] [Baidu Cloud Link]
[References][Google Drive Link] [Baidu Cloud Link]
[Challenging Set][Google Drive Link] [Baidu Cloud Link]
Passwords: Raw: 1234567; Reference: 8901234; Challenging: 5678901
C. Li, C. Guo, W. Ren, R. Cong, J. Hou, S. Kwong, D. Tao, “An Underwater Image Enhancement Benchmark Dataset and Beyond,” IEEE Trans. Image Process., vol. 29, pp.4376-4389, 2019.
If you use this dataset, please cite the related paper. Thanks.
We released our TIP 2016 underwater image enhancement code. Have fun! [Github Link]
If you use this code, please cite the related papers. Thanks.
C. Li, J. Guo, R. Cong, et al., “Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior,” IEEE Trans. Image Process., vol. 25, no. 12, pp.5664-5677, 2016.
C. Li, J. Guo, S. Chen, et al., “Underwater image restoration based on minimum information loss principle and optical properties of underwater imaging”, IEEE ICIP , pp. 1993-1997 (2016).