TADSRNet: A triple-attention dual-scale residual network for super-resolution image quality assessment
Author:
Funder
National Natural Science Foundation of China
Natural Science Foundation of Shaanxi Province
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-023-04932-7.pdf
Reference46 articles.
1. Yang W, Zhou F, Zhu R, Fukui K, Wang G, Xue J-H (2019) Deep learning for image super-resolution. Neurocomputing
2. Yang W, Zhang X, Tian Y, Wang W, Xue J-H, Liao Q (2019) Deep learning for single image super-resolution: A brief review. IEEE Trans Multimed, 21(12):3106–3121
3. Zhang K, Luo S, Li M, Jing J, Lu J, Xiong Z (2020) Learning stacking regressors for single image super-resolution. Appl Intell, 50(12):4325–4341
4. Zhang Y, Tian Y, Kong Y, Zhong B, Fu Y (2018) Residual dense network for image super-resolution. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 2472–2481
5. Li M, Ma B, Liu Y, Zhang Y (2022) s-lmpnet: a super-lightweight multistage progressive network for image super-resolution. Appl Intell, 1-20
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