A Channel Contrastive Attention-Based Local-Nonlocal Mutual Block on Super-Resolution
Author:
Affiliation:
1. College of Information Engineering, Shanghai Maritime University
2. School of Mechanical Engineering, University of Shanghai for Science and Technology
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Link
https://www.jstage.jst.go.jp/article/transinf/E107.D/9/E107.D_2024EDP7009/_pdf
Reference25 articles.
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2. [2] J. Kim, J.K. Lee, and K.M. Lee, “Accurate image super-resolution using very deep convolutional networks,” Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.1646-1654, 2016.
3. [3] B. Lim, S. Son, H. Kim, S. Nah, and K.M. Lee, “Enhanced deep residual networks for single image super-resolution,” Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.136-144, 2017.
4. [4] Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y. Fu, “Image super-resolution using very deep residual channel attention networks,” Proc. European Conference on Computer Vision (ECCV), pp.294-310, 2018. 10.1007/978-3-030-01234-2_18
5. [5] S. Woo, J. Park, J.-Y. Lee, and I.S. Kweon, “Cbam: Convolutional block attention module,” Proc. European conference on computer vision (ECCV), pp.3-19, 2018. 10.1007/978-3-030-01234-2_1
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