Effective polarization-based image dehazing through 3D convolution network-Reference-Cited by-同舟云学术

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Effective polarization-based image dehazing through 3D convolution network

Published:2024-04-09 Issue:S1 Volume:18 Page:463-474
ISSN:1863-1703
Container-title:Signal, Image and Video Processing
language:en
Short-container-title:SIViP

Author:

Wang Xin,Fu Wei,Yu Haichao,Zhang Yong

Funder

National Natural Science Foundation of China

Chinese Aeronautical Establishment

Natural Science Foundation of Anhui Province

Fundamental Research Funds for the Central Universities

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11760-024-03147-8.pdf

Reference66 articles.

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2. Sakaridis, C., Dai, D., Hecker, S., et al.: Model adaptation with synthetic and real data for semantic dense foggy scene understanding. In: Proceedings of the European conference on computer vision (ECCV), pp. 687–704 (2018)

3. Cui, J., Zhao, Y., Dong, X., et al.: Unsupervised segmentation of multiview feature semantics by hashing model. Signal Process. 160, 106–112 (2019)

4. Chen, Y., Li, W., Chen, X., et al.: Learning semantic segmentation from synthetic data: A geometrically guided input-output adaptation approach. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1841–1850 (2019)

5. Mehra, A., Mandal, M., Narang, P., et al.: ReViewNet: a fast and resource optimized network for enabling safe autonomous driving in hazy weather conditions. IEEE Trans. Intell. Transp. Syst. 22(7), 4256–4266 (2020)

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