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