Open Access
| Issue |
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
|
|
|---|---|---|
| Article Number | 04016 | |
| Number of page(s) | 7 | |
| Section | Foundations and Frontiers in Multimodal AI, Large Models, and Generative Technologies | |
| DOI | https://doi.org/10.1051/itmconf/20257804016 | |
| Published online | 08 September 2025 | |
- R. Keys, “Cubic convolution interpolation for digital image processing,” IEEE Trans. Acoust., Speech, Signal Process., 1981. [Google Scholar]
- J. Yang, J. Wright, T. S. Huang and Y. Ma, “Image super-resolution via sparse representation,” IEEE Trans. Image Process., 2010. [Google Scholar]
- C. Dong, C. C. Loy, K. He and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell., 2016. [Google Scholar]
- J. Kim, J. K. Lee and K. M. Lee, “Accurate image super-resolution using very deep convolutional networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2016. [Google Scholar]
- C. Ledig, L. Theis, F. Huszár et al., “Photo-realistic single image super-resolution using a generative adversarial network,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2017. [Google Scholar]
- X. Wang, K. Yu, S. Wu et al., “ESRGAN: Enhanced super-resolution generative adversarial networks,” in Proc. Eur. Conf. Comput. Vis. (ECCV) Workshops, 2018. [Google Scholar]
- Y. Zhang, K. Li, K. Li et al., “Image super-resolution using very deep residual channel attention networks,” in Proc. Eur. Conf. Comput. Vis. (ECCV), 2018. [Google Scholar]
- T. Dai, J. Cai, Y. Zhang, S. Xia and L. Zhang, “Second-order attention network for single image super-resolution,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2019. [Google Scholar]
- H. Wang, Y. Li, Y. Wang and L. Liu, “EA-SRGAN: Edge-aware generative adversarial network for image super-resolution,” IEEE Trans. Image Process., 2021. [Google Scholar]
- Z. Chen, H. Zhang, Y. Wang and Q. Zhang, “Multi-scale attention network for image super-resolution,” Neurocomputing, 2022. [Google Scholar]
- C. Zhang, L. Zhu and L. Yu, “A review of attention mechanisms in convolutional neural networks,” Comput. Eng. Appl., vol. 57, no. 20, pp. 64–72, 2021 [Google Scholar]
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