The SUIM-E dataset is created by supplementing the SUIM dataset with the corresponding enhancement references.
We used 12 underwater image enhancement methods to generate candidate reference images, including CE[1], Fusion [2], GCHE [3], HistogramPiror [4], HUE [5], IBLA [6], Retinex [7], TwoStep [8], UCM [9], ULAP[10]), DCP [11] and a commercial application for enhancing underwater images (i.e., dive+ [12] ). During the whole voting phase on SUIM dataset, the distribution of votes received by different underwater enhancement methods and the percentages of the reference images from the results of different methods are shown below.
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| The percentages of votes received by different underwater enhancement methods in the vote on the whole dataset. | The percentages of the reference images from the results of different methods. |
The SUIM-E dataset contains a total of 1635 real-world underwater images, along with the corresponding high-quality reference images and the pixel annotations for eight object categories: fish (vertebrates), reefs (invertebrates), aquatic plants, wrecks/ruins, human divers, robots, and seafloor are in it. To the best of our knowledge, it is the first real-world underwater dataset that contains both corresponding enhancement reference and semantic segmentation map.
BaiduCloud Link (Extraction code: 6uaf)
@article{qi2022sguie,
title={SGUIE-Net: Semantic Attention Guided Underwater Image Enhancement with Multi-Scale Perception},
author={Qi, Qi and Li, Kunqian and Zheng, Haiyong and Gao, Xiang and Hou, Guojia and Sun, Kun},
journal={arXiv preprint arXiv:2201.02832},
year={2022}
}
@inproceedings{islam2020suim,
title={{Semantic Segmentation of Underwater Imagery: Dataset and Benchmark}},
author={Islam, Md Jahidul and Edge, Chelsey and Xiao, Yuyang and Luo, Peigen and Mehtaz,
Muntaqim and Morse, Christopher and Enan, Sadman Sakib and Sattar, Junaed},
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year={2020},
organization={IEEE/RSJ}
}
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[12] https://diveplus.cn/app