Multi-Scale Image Super-Resolution via Hierarchical Filter Groups
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-021-02832-2.pdf
Reference43 articles.
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2. Amaranageswarao G, Deivalakshmi S, Ko SB (2020) Residual learning based densely connected deep dilated network for joint deblocking and super resolution. Applied Intelligence https://doi.org/10.1007/s10489-020-01670-y
3. Amaranageswarao, G, Deivalakshmi, S, Ko, SB (2020) Wavelet based medical image super resolution using cross connected residual-in-dense grouped convolutional neural network. Journal of Visual Communication and Image Representation 70:102819, https://doi.org/10.1016/j.jvcir.2020.102819, http://www.sciencedirect.com/science/article/pii/S1047320320300699
4. Bevilacqua, M, Roumy, A, Guillemot, C, line Alberi Morel M (2012) Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: Proceedings of the British Machine Vision Conference, BMVA Press, pp 135.1–135.10, https://doi.org/10.5244/C.26.135
5. Choi J, Kim M (2017) A deep convolutional neural network with selection units for super-resolution. In: 2017 IEEE Conference on computer vision and pattern recognition workshops (CVPRW), pp 1150–1156, https://doi.org/10.1109/CVPRW.2017.153
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