Open Access
| Issue |
ITM Web Conf.
Volume 80, 2025
2025 2nd International Conference on Advanced Computer Applications and Artificial Intelligence (ACAAI 2025)
|
|
|---|---|---|
| Article Number | 01003 | |
| Number of page(s) | 8 | |
| Section | Machine Learning & Deep Learning Algorithms | |
| DOI | https://doi.org/10.1051/itmconf/20258001003 | |
| Published online | 16 December 2025 | |
- Z. Wang, J. Chen, S.C. Hoi, Deep learning for image super-resolution: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3365–3387 (2020) [Google Scholar]
- R. Keys, Cubic convolution interpolation for digital image processing. IEEE Trans. Acoust. Speech Signal Process. 29(6), 1153–1160 (2003) [Google Scholar]
- J. Yang, J. Wright, T.S. Huang, Y. Ma, Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010) [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
- C. Dong, C.C. Loy, K. He, X. Tang, Learning a deep convolutional network for image super-resolution, in Proceedings of the European Conference on Computer Vision (ECCV), 184–199 (2014) [Google Scholar]
- C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, … W. Shi, Photo-realistic single image super-resolution using a generative adversarial network, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4681–4690 (2017) [Google Scholar]
- X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, C. Dong, … C. Change Loy, ESRGAN: Enhanced super-resolution generative adversarial networks, in Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 0–0 (2018) [Google Scholar]
- X. Wang, L. Xie, C. Dong, Y. Shan, Real-ESRGAN: Training real-world blind super- resolution with pure synthetic data, in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 1905–1914 (2021) [Google Scholar]
- Y. Zhang, Y. Tian, Y. Kong, B. Zhong, Y. Fu, Residual dense network for image super-resolution, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2472–2481 (2018) [Google Scholar]
- J. Liang, J. Cao, G. Sun, K. Zhang, L. Van Gool, R. Timofte, SwinIR: Image restoration using Swin Transformer, in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 1833–1844 (2021) [Google Scholar]
- X. Chen, X. Wang, J. Zhou, Y. Qiao, C. Dong, Activating more pixels in image super- resolution transformer, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 22367–22377 (2023) [Google Scholar]
- K. Zhang, W. Zuo, L. Zhang, Deep plug-and-play super-resolution for arbitrary blur kernels, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1671–1681 (2019) [Google Scholar]
- Y. Blau, T. Michaeli, The perception-distortion tradeoff, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6228–6237 (2018) [Google Scholar]
- R. Zhang, P. Isola, A.A. Efros, E. Shechtman, O. Wang, The unreasonable effectiveness of deep features as a perceptual metric, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 586–595 (2018) [Google Scholar]
- J. Kim, J.K. Lee, K.M. Lee, Accurate image super-resolution using very deep convolutional networks, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1646–1654 (2016) [Google Scholar]
- B. Lim, S. Son, H. Kim, S. Nah, K. Mu Lee, Enhanced deep residual networks for single image super-resolution, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 136–144 (2017) [Google Scholar]
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