Graph-Based Representation for Multi-image Super-Resolution

Author:

Tarasiewicz TomaszORCID,Kawulok MichalORCID

Publisher

Springer Nature Switzerland

Reference18 articles.

1. An, T., Zhang, X., Huo, C., Xue, B., Wang, L., Pan, C.: TR-MISR: multiimage super-resolution based on feature fusion with transformers. IEEE J-STARS 15, 1373–1388 (2022)

2. Deudon, M., Kalaitzis, A., et al.: HighResnet: recursive fusion for multi-frame super-resolution of satellite imagery. arXiv preprint arXiv:2002.06460 (2020)

3. Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch Geometric. In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)

4. Fey, M., Lenssen, J.E., Weichert, F., Müller, H.: SplineCNN: fast geometric deep learning with continuous B-spline kernels. In: Proceedings of the IEEE CVPR, pp. 869–877 (2018)

5. Guizar-Sicairos, M., Thurman, S.T., Fienup, J.R.: Efficient subpixel image registration algorithms. Opt. Lett. 33(2), 156–158 (2008)

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