https://github.com/openspyrit/spyrit-examples
SPYRIT examples including tutorials and research contributions
https://github.com/openspyrit/spyrit-examples
image-reconstruction notebook research-paper spyrit tutorial
Last synced: 2 days ago
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SPYRIT examples including tutorials and research contributions
- Host: GitHub
- URL: https://github.com/openspyrit/spyrit-examples
- Owner: openspyrit
- License: lgpl-3.0
- Created: 2021-01-28T04:02:06.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2026-04-09T08:03:36.000Z (13 days ago)
- Last Synced: 2026-04-09T10:07:07.904Z (13 days ago)
- Topics: image-reconstruction, notebook, research-paper, spyrit, tutorial
- Language: Jupyter Notebook
- Homepage:
- Size: 55.8 MB
- Stars: 16
- Watchers: 4
- Forks: 8
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
README
# spyrit-examples
## List of research contributions
Some of research contributions based on the SPyRiT package:
### 2025
* [Hyperspectral LSFM](./2025_hLSFM/) (2025)
> S Crombez, C Ray, C Exbrayat-Héritier, F Ruggiero, NDucros, "Hyperspectral LSFM with DMD-only Shaping and Neural Network Reconstruction," (2025). [main PDF](https://hal.science/hal-04824372/document), [supplementary PDF](https://hal.science/hal-04824372/preview/hspim_nature_supp.pdf).
* [SPyRiT 3.0](./2025_spyrit_v3/) (Optics Express, 2025)
> JFJP Abascal, T Baudier, R Phan, A Repetti, N Ducros, "SPyRiT 3.0: an open source package for single-pixel imaging based on deep learning," Optics Express (2025). [DOI](https://doi.org/10.1364/OE.559227), [main PDF](https://hal.science/hal-04662876/document), [supplementary PDF](https://hal.science/hal-04662876/preview/supplementary_v4.pdf).
### 2023
* [OpenSpyrit ecosystem](./2022_OE_spyrit2/) (Optics Express, 2023)
> G. Beneti-Martin, L Mahieu-Williame, T Baudier, N Ducros, "OpenSpyrit: an Ecosystem for Reproducible Single-Pixel Hyperspectral Imaging," Optics Express, Vol. 31, No. 10, (2023). [DOI](https://doi.org/10.1364/OE.483937).
### 2022
* [ISTE Book Chapter](./deprecated/2022_ISTE/) (ISTE, 2022)
> [*English*] N Ducros. An Introduction to Single-Pixel Imaging. [DOI](https://doi.org/10.1002/9781394283996.ch8)
> [*French*] N Ducros. Une introduction à l’imagerie computationnelle monodétecteur. [DOI](https://doi.org/10.51926/ISTE.9132.ch8).
### 2021
* [Denoised Completion Network (DC-Net)](./deprecated/2021_Optics_express/) (Optics Express, 2021)
> Antonio Lorente Mur, Pierre Leclerc, Françoise Peyrin, Nicolas Ducros. "Single-Pixel Image Reconstruction from Experimental Data Using Neural Networks," Vol. 29, No. 11, [DOI](https://doi.org/10.1364/OE.424228). [PDF](https://hal.archives-ouvertes.fr/hal-03202353/document).
### 2020
* [Completion Network (C-Net)](./deprecated/2020_ISBI_CNet/) (ISBI, 2020)
> Nicolas Ducros, A Lorente Mur, F. Peyrin. "A Completion Network for Reconstruction from Compressed Acquisition." 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Apr 2020, Iowa City, United States, pp.619-623. [DOI](10.1109/ISBI45749.2020.9098390). [PDF](https://hal.archives-ouvertes.fr/hal-02342766/document/).
## List of educational materials
Some of educational material based on the SPYRIT toolbox
### 2025
* [Deep image reconstruction using the PyTorch and SPyRiT 3.0 packages](./2025_DLMIS/readMe.md) (DLMIS 25)
> N Ducros, S Hariga, T Kaprelian, L Leon, T Maitre. Hands-on session 4. [Deep Learning for Medical Imaging School 2025](https://deepimaging2025.sciencesconf.org/).
### 2023
* [Deep image reconstruction using the PyTorch and SPyRiT packages](./2023_DLMIS/readMe.md) (DLMIS 23)
> L Amador, E Chen, N Ducros, H-J Ling, K Mom, J Puig, T Grenier, E Saillard. Hands-on session 4. [Deep Learning for Medical Imaging School 2023](https://deepimaging2023.sciencesconf.org/).
### 2021
* [Deep image reconstruction using the PyTorch and SPyRiT packages](./deprecated/2021_DLMIS_Hands-on/readMe.md) (DLMIS 21)
> N Ducros, T Leuliet, A Lorente Mur, L Friot--Giroux. Hands-on session 3.1. [Deep Learning for Medical Imaging School 2021](https://deepimaging2021.sciencesconf.org/).