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In image Super-Resolution (SR), relying on large datasets for training is a double-edged sword.
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Bashir, S.M.A., Wang, Y., Khan, M., Niu, Y.: A comprehensive review of deep learning-based single image super-resolution. PeerJ Computer Science 7
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Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., Timofte, R.: Swinir: Image restoration using swin transformer. In: ICCV. pp. 1833–1844 (2021)
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Cazenavette, G., Wang, T., Torralba, A., Efros, A.A., Zhu, J.Y.: Generalizing dataset distillation via deep generative prior. In: CVPR. pp. 3739–3748 (2023)
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Chen, X., Wang, X., Zhou, J., Qiao, Y., Dong, C.: Activating more pixels in image super-resolution transformer. In: CVPR. pp. 22367–22377 (2023)
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Ding, Q., Liang, Z., Wang, L., Wang, Y., Yang, J.: Not all patches are equal: Hierarchical dataset condensation for single image super-resolution. IEEE Signal Processing Letters (2023)
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Moser, B.B., Frolov, S., Raue, F., Palacio, S., Dengel, A.: Dwa: Differential wavelet amplifier for image super-resolution. pp. 232–243. Springer (2023)
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2024
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