Lightweight single image super-resolution based on multi-path progressive feature fusion and attention mechanism
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Link
https://link.springer.com/content/pdf/10.1007/s13042-023-01847-0.pdf
Reference61 articles.
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2. Ahn N, Kang B, Sohn KA (2018) Fast, accurate, and lightweight super-resolution with cascading residual network. In: 15th European Conference on computer vision (ECCV). Springer, Munich, pp 256–272. https://doi.org/10.1007/978-3-030-01249-6_16
3. Bevilacqua M, Roumy A, Guillemot C, et al (2012) Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: 23rd British Amchine Vision Conference (BMVC). https://doi.org/10.5244/C.26.135
4. Cao C, Liu X, Yang Y, et al (2015) Look and think twice: capturing top-down visual attention with feedback convolutional neural networks. In: IEEE International Conference on computer vision (ICCV). Springer, Santiago, pp 2956–2964. https://doi.org/10.1109/ICCV.2015.338
5. Dai T, Cai JR, Zhang YB, et al (2019) Second-order attention network for single image super-resolution. In: IEEE/CVF Conference on computer vision and pattern recognition (CVPR). IEEE, pp 11057–11066. https://doi.org/10.1109/CVPR.2019.01132
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