See-the-Invisible-with-SWIR

See the Invisible with SWIR Models

[Paper] [Code]

Official checkpoint collection for the SWIR denoising method and its reported baselines and ablations. Download and verify every published artifact with:

pip install "huggingface-hub>=0.25"
python scripts/download_artifacts.py --kind models

The exact publication allowlist, SHA-256 hashes, embedded metadata, configurations, and legacy script identifiers are included in checkpoint_inventory.json.

PG, ELD, SFRN, single-stage, and no-FPN artifacts have unique legacy namespaces. The full-method and shot-noise trainers accidentally shared one output namespace; that surviving weight is labeled two_stage_shared_task, not uniquely as either experiment. See docs/checkpoint_provenance.md before citing or repackaging it.

Model weights are released under the repository's MIT license. Training data are separately licensed; SID images are not redistributed.

@inproceedings{jiang2025see,
  title={See the Invisible with SWIR: Learning to Enhance via Synthetic Noise Modeling},
  author={Jiang, Haiyang and Wang, Hongjun and Zheng, Yinqiang},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops},
  pages={7449--7458},
  year={2025}
}