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---
license: mit
---
# Description
This repository contains a series of weights for adapting the DRUNet denoiser
in order to be able to work with hyperspectral images.
These weights are meant to be used with the `hypnp` library:
<http://github.com/Danaroth83/hypnp>
In particular the weights contained in this folder are associated to the following adapting architecture:
- `20251025_174116_606035` - `projection_encoder`: An encoding/decoder network.
- `20251029_110345_695678` - `projection_qr`: A QR decomposition encoder and deep decoder network.
- `20251026_035736_830488` - `grouper_arranger`: A band selection module.
- `20251026_044313_346452` - `grouper_arranger_skip`: A band selection module with skip attention network.
- `20251026_141518_492696` - `film_middle`: A FiLM that hooks middle layers of the DRUNet. Baseline result.
- `20251026_063231_578169` - `film_middle_qr`: FiLM network with QR projection of the input.
- `20251027_091506_635645` - `film_middle_qr_groups_10`: FiLM network with QR projection, with inputs passed sequentially in groups of 10.
- `20251029_093111_154168` - `film_no_head`: FiLM network without trained head in DRUNet.
- `20251031_173527_494979` - `film_full`: FiLM full network
- `20251102_135320_481765` - `lora_pca_big`: LoRA network applied on a PCA decomposition.
- `20251104_192334_965269` - `cave_projection_matrix_orthogonal`: Linear channel projection, with orthogonal matrix.
- `20251105_024251_967712` - `cave_projection_matrix_orthogonal_compressed`: Linear channel projection matrix to 3 channels. Matrix is constrained to be orthogonal.
- `20251104_200826_195587` - `cave_projection_matrix_simplex`: Linear channel projection matrix constrained to be positive with sum-to-one condition.
- `20251104_230322_748168` - `cave_projection_matrix_simplex_compressed`: Linear channel projection matrix to 3 channels. Matrix is constrained to be positive with sum-to-one condition.
# Credits
These weights were produced by:
Daniele Picone
Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, 38000 Grenoble, France
Mail: [daniele.picone@grenoble-inp.fr](mailto:daniele.picone@grenoble-inp.fr)
Mohamad Jouni
Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, 38000 Grenoble, France
Mail: [mohamad.jouni@grenoble-inp.fr](mailto:daniele.picone@grenoble-inp.fr) |