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:

@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},
}