A deraining with detail-recovery network via context aggregation-Reference-Cited by-同舟云学术

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A deraining with detail-recovery network via context aggregation

Published:2023-06-17 Issue:5 Volume:29 Page:2591-2601
ISSN:0942-4962
Container-title:Multimedia Systems
language:en
Short-container-title:Multimedia Systems

Author:

Gao Weihao,Zhang Yongjun,Long Wei,Cui Zhongwei

Funder

Natural science research project of Guizhou Provincial Department of Education

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Information Systems,Software

Link

https://link.springer.com/content/pdf/10.1007/s00530-023-01116-8.pdf

Reference42 articles.

1. Zamanakos, G., Tsochatzidis, L., Amanatiadis, A., Pratikakis, I.: A comprehensive survey of lidar-based 3d object detection methods with deep learning for autonomous driving. Comput. Grap. 99, 153–181 (2021)

2. Mei, R., Wang, H., Men, A.: Attention-enhanced and more balanced r-cnn for object detection. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 2136–2140 . IEEE (2020)

3. Vanian, V., Zamanakos, G., Pratikakis, I.: Improving performance of deep learning models for 3d point cloud semantic segmentation via attention mechanisms. Comput. Graph. 106, 277–287 (2022)

4. Kang, L.-W., Lin, C.-W., Fu, Y.-H.: Automatic single-image-based rain streaks removal via image decomposition. IEEE Trans. Image Process. 21(4), 1742–1755 (2011)

5. Huang, D.-A., Kang, L.-W., Wang, Y.-C.F., Lin, C.-W.: Self-learning based image decomposition with applications to single image denoising. IEEE Trans. Multimedia 16(1), 83–93 (2013)

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