| --- |
| language: en |
| license: cc-by-nc-sa-4.0 |
| task_categories: |
| - image-to-image |
| tags: |
| - remote-sensing |
| - underwater |
| - image-restoration |
| --- |
| # KAUSTics |
|
|
| Semi-synthetic paired through-water UAV restoration dataset from32 source images, with1280 calm-water reference patches and12800 distorted views. References are calm-water appearance targets, not independently verified water-free geometry or reflectance. |
|
|
| ## Availability and files |
|
|
| The complete prepared release includes both `original/` (original patch bytes, nominally1000×1000) and `rgb256/` (the exact RGB256 cache used for final experiments). No patch regeneration or additional rendering was performed. One original degraded view is1344×1344 rather than1000×1000; the256 cache standardizes all images. If an initial public upload contains only256, this paragraph must be updated to reflect that partial availability. |
|
|
| `manifest_256.json` and `manifest_original.json` pair each reference with ten views: caustics configurations1/2 ×base, blur, color1, color2, color3. Blender supplies caustics/refraction effects; blur and colour modifications are empirical proxies for turbidity and absorption, not measured depth/scattering coefficients. No calibrated depth labels are provided. `source_assignments.csv` and the manifests define the exact partition. |
|
|
| | Split | UAV source IDs | Reference patches | Distorted views | |
| |---|---:|---:|---:| |
| | Train |26|1040|10400| |
| | Validation |3|120|1200| |
| | Test |3|120|1200| |
|
|
| All patches and variants from a source remain together. Source IDs are disjoint; geographic footprint independence has not been independently established. Do not repartition individual patches or variants randomly. Reference pixels are repeated across degraded views; count each reference once when reporting dataset area. |
|
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| Load with the KAUSTics-Net code’s `Pairs` dataset. Training samples2080 base pairs per epoch, replacing the input with an augmentation variant with probability0.6. Validation/test enumerate all degraded views. Training and evaluation use256RGB images normalized to[-1,1]. Original and256 images are alternative representations of the same patches, not independent extra samples. |
|
|
| ## License |
|
|
| The dataset is 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} |
| } |
| ``` |
|
|
