Underwater Image Enhancement Using Convolutional Neural Network and the MultiUnet Model
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-64067-4_4
Reference21 articles.
1. Han, G., Wang, M., Zhu, H., Lin, C.: UIEGAN: adversarial learning-based photorealistic image enhancement for intelligent underwater environment perception. IEEE Trans. Geosci. Remote Sens. 61, 1–14 (2023). Art no. 5611514, https://doi.org/10.1109/TGRS.2023.3281741
2. Fu, Z., Lin, X., Wang, W., Huang, Y., Ding, X.: Underwater image enhancement via learning water type desensitized representations. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, pp. 2764–2768 (2022). https://doi.org/10.1109/ICASSP43922.2022.9747758
3. Lin, Y., Shen, L., Wang, Z., Wang, K., Zhang, X.: Attenuation coefficient guided two-stage network for underwater image restoration. IEEE Signal Process. Lett. 28, 199–203 (2021). https://doi.org/10.1109/LSP.2020.3048619
4. https://www.kaggle.com
5. Dhar, S., Roy, H., Mukhopadhyay, A., Kundu, A., Ghosh, A., Roy, S.: Interpretable image enhancement based on convolutional neural network. In: 2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), Bangalore, India, pp. 6–11 (2020). https://doi.org/10.1109/ICRCICN50933.2020.9296201
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