A Dual-Branch Algorithm for Semantic-Focused Face Super-Resolution Reconstruction-Reference-Cited by-同舟云学术

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A Dual-Branch Algorithm for Semantic-Focused Face Super-Resolution Reconstruction

Published:2024-08-01 Issue:8 Volume:E107.A Page:1435-1439
ISSN:0916-8508
Container-title:IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
language:en
Short-container-title:IEICE Trans. Fundamentals

Author:

QI Qi¹,MENG Liuyi²,XU Ming³,BAI Bing⁴

Affiliation:

1. Department of Decision Consulting, Party School of Liaoning Provincial Party Committee

2. School of Robotics Science and Engineering, Northeastern University

3. School of Artificial Intelligence, Shenyang University of Technology

4. Department of Leadership Science, Party School of Liaoning Provincial Party Committee

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Link

https://www.jstage.jst.go.jp/article/transfun/E107.A/8/E107.A_2024EAL2004/_pdf

Reference13 articles.

1. [1] C. Chen, D. Gong, H. Wang, Z. Li, and K.-Y.K. Wong, “Learning spatial attention for face super-resolution,” IEEE Trans. Image Process., vol.30, pp.1219-1231, 2021. 10.1109/tip.2020.3043093

2. [2] I. Goodfellow, J. Pouget-Abadie, M. Mirza, et al., “Generative adversarial nets,” Advances in Neural Information Processing Systems, 27, 2014.

3. [3] S. Menon, A. Damian, S. Hu, N. Ravi, and C. Rudin, “Pulse: Self-supervised photo upsampling via latent space exploration of generative models,” Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.2437-2445, 2020. 10.1109/cvpr42600.2020.00251

4. [4] C. Ma, Z. Jiang, Y. Rao, J. Lu, and J. Zhou, “Deep face super-resolution with iterative collaboration between attentive recovery and landmark estimation,” Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.5569-5578, 2020. 10.1109/cvpr42600.2020.00561

5. [5] X. Wang, Y. Li, H. Zhang, and Y. Shan, “Towards real-world blind face restoration with generative facial prior,” Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.9168-9178, 2021. 10.1109/cvpr46437.2021.00905

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