FFEINR: flow feature-enhanced implicit neural representation for spatiotemporal super-resolution
Author:
Funder
National Key R &D Program of China
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12650-024-00959-1.pdf
Reference47 articles.
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2. Barron JT, Mildenhall B, Tancik M, et al (2021) Mip-NeRF: a multiscale representation for anti-aliasing neural radiance fields. In: Proceedings of IEEE/CVF international conference on computer vision, pp 5835–5844
3. Bashir SMA, Wang Y, Khan M et al (2021) A comprehensive review of deep learning-based single image super-resolution. PeerJ Comput Sci 7:e621
4. Cai S, Mao Z, Wang Z et al (2021) Physics-informed neural networks (PINNs) for fluid mechanics: a review. Acta Mech Sin 37(12):1727–1738
5. Chen A, Xu Z, Zhao F, et al (2021a) MVSNeRF: fast generalizable radiance field reconstruction from multi-view stereo. In: Proceedings of IEEE/CVF international conference on computer vision, pp 14104–14113
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