LBCRN: lightweight bidirectional correction residual network for image super-resolution-Reference-Cited by-同舟云学术

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LBCRN: lightweight bidirectional correction residual network for image super-resolution

Published:2023-02-09 Issue:1 Volume:34 Page:341-364
ISSN:0923-6082
Container-title:Multidimensional Systems and Signal Processing
language:en
Short-container-title:Multidim Syst Sign Process

Author:

Huang Shuying,Wang Jichao,Yang Yong,Wan Weiguo,Li Guoqiang

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Artificial Intelligence,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing,Software

Link

https://link.springer.com/content/pdf/10.1007/s11045-023-00866-y.pdf

Reference59 articles.

1. Agustsson, E., & Timofte, R. (2017). NTIRE 2017 challenge on single image super-resolution: Dataset and study. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (CVPRW) (pp. 126–135).

2. Ahn, N., Kang, B., & Sohn, K.-A. (2018). Fast, accurate, and lightweight super-resolution with cascading residual network. In Proceedings of the European conference on computer vision (ECCV) (pp. 252–268).

3. Anwar, S., Khan, S., & Barnes, N. (2020). A deep journey into super-resolution: A survey. arXiv:1904.07523

4. Bevilacqua, M., Roumy, A., Guillemot, C., & Morel, M. A. (2012). Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In Proceedings of the British machine vision conference (pp. 1–10).

5. Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., & Chua, T.-S. (2017). SCA-CNN: Spatial and channel-wise attention in convolutional networks for image captioning. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 5659–5667).

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1. ULSR-UV: an ultra-lightweight super-resolution networks for UAV video;The Journal of Supercomputing;2024-05-30

2. TARN: a lightweight two-branch adaptive residual network for image super-resolution;International Journal of Machine Learning and Cybernetics;2024-04-12

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