Underwater Target Detection Based on Dual Inversion Dehazing and Multi-Scale Feature Fusion Network
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-1010-2_8
Reference19 articles.
1. Li, H., Zhuang, P., Wei, W., Li, J.: Underwater image enhancement based on dehazing and color correction. In: 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), Xiamen, China, pp. 1365–1370 (2019). https://doi.org/10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00196
2. Li, R., Dong, H., Wang, L., Liang, B., Guo, Y., Wang, F.: Frequency-aware deep dual-path feature enhancement network for image dehazing. In: 2022 26th International Conference on Pattern Recognition (ICPR), Montreal, QC, Canada, pp. 3406–3412 (2022). https://doi.org/10.1109/ICPR56361.2022.9955635
3. He, L., Yan, Y., Cai, C.: Underwater image enhancement based on light attenuation prior and dual-image multi-scale fusion. In: 2022 Global Conference on Robotics, Artificial Intelligence and Information Technology (GCRAIT), Chicago, IL, USA, pp. 478–482 (2022). https://doi.org/10.1109/GCRAIT55928.2022.00105
4. Berman, D., Levy, D., Avidan, S., Treibitz, T.: Underwater single image color restoration using haze-lines and a new quantitative dataset. IEEE Trans. Pattern Anal. Mach. Intell. 43(8), 2822–2837 (2021). https://doi.org/10.1109/TPAMI.2020.2977624
5. Desai, C., Tabib, R.A., Reddy, S.S., Patil, U., Mudenagudi, U.: RUIG: realistic underwater image generation towards restoration. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Nashville, TN, USA, pp. 2181–2189 (2021). https://doi.org/10.1109/CVPRW53098.2021.00247
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