---
license: cc-by-nc-4.0
---

# Description

Series of weights recovered by training the `ligthning` torch model 
UnrolledSystem implemented in:
(Unrolled demosaicking)[https://github.com/mattmull42/unrolled_demosaicking]

The network was trained over 15 color filter array patterns:
- `bayer`
- `binning`
- `chakrabarti`
- `gindele`
- `hamilton`
- `honda`
- `honda2`
- `kaizu`
- `kodak`
- `luo`
- `quad_bayer`
- `random`
- `sparse_3`
- `wang`

The network is based on U-NET, unrolled over 4 stages, and plugged into an ADMM solver. The network is trained over 300 natural images, cut into patches of size 64 x 64.

The three versions given in this repository are:
- `4`: Baseline weights.
- `4B`: Variant with different training set.
- `4V`: Introduces geometric transformations on the patterns.

# Citation

If you use this dataset, please cite:
```bibtex
@InProceedings{muller_eusipco_2024,
  author    = {Muller, Matthieu and Picone, Daniele and Dalla Mura, Mauro and Ulfarsson, Magnus Orn},
  booktitle = {European Signal Processing Conference ({EUSIPCO})},
  title     = {Pattern-invariant unrolling for robust demosaicking},
  year      = {2024},
}
```