Add KAUSTics-Net preprint citation and license to release card

README.md

@@ -1,5 +1,6 @@
 ---
 language: en
+license: cc-by-nc-sa-4.0
 library_name: pytorch
 pipeline_tag: image-to-image
 tags:
@@ -29,6 +30,21 @@ KAUSTics training uses source-disjoint26/3/3 UAV source IDs. Sea-Undistort fine-
 
 These weights target the paper’s dataset/task. Restoration quality and geometric accuracy on unrelated real UAV scenes are not guaranteed. Sea-Undistort adaptation shows a fidelity/perceptual trade-off; it is not a claim of universally improved image quality.
 
-Authors: Dario Scilla, Omar A. Lopez, Kasper Johansen, Brian O. Nieuwenhuis and Matthew F. McCabe. Paper: *KAUSTics-Net: Diffusion-Based Through-Water UAV Restoration with a Semi-Synthetic Dataset* (revision manuscript).
+## License
 
-Licensing for the authors’ model weights will be specified by the authors before publication; this prepared card does not assign a license. Dataset terms remain separate.
+The model weights are licensed under [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/).
+
+## Citation
+
+If you use this release in your research, please cite our [arXiv preprint](https://arxiv.org/abs/2611.03120).
+
+```bibtex
+@article{scilla2026kausticsnet,
+  title   = {{KAUSTics-Net}: Diffusion-Based Through-Water {UAV} Restoration with a Semi-Synthetic Dataset},
+  author  = {Scilla, Dario and Lopez, Omar A. and Johansen, Kasper and Nieuwenhuis, Brian O. and McCabe, Matthew F.},
+  journal = {arXiv preprint arXiv:2611.03120},
+  year    = {2026},
+  doi     = {10.48550/arXiv.2611.03120},
+  url     = {https://arxiv.org/abs/2611.03120}
+}
+```

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