EigenPSFExtactor allows you to finely characterization of your optical system from images of isolated sources (fluorescent microbeads, guide stars, ...)
EigenPSFExtactor allows you to finely characterize your optical system from images of isolated sources (fluorescent microbeads, guide stars, ...)
It based on a set of accuracte routines to detect, register, process and average the diffracted sources images.
It based on a set of accuracte routines to detect, register, process and average the diffracted sources images.
The plugin is rock solid for 2D images.
The plugin is rock solid for 2D images.
Beware: the treatment of 3D images is slow, because this version is based on Java only. Use it at your own risks.
**Beware:** the treatment of 3D images is slow, because this version is based on Java only. Use it at your own risks.
## Citation
## Citation
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## How it works?
## How it works?
1. Open Fiji
1. Open Fiji
2. Open Plugins -> Eigen_PSF -> Eigen_PSF
2. Open *Plugins -> Eigen_PSF -> Eigen_PSF*
3. Drag and drop the images you want to open in the left pane.
3.**Drag and drop the images** you want to open in the left pane.
4.You can display the selected images by clicking on the eye (show) in the left pane.
4.**Display the selected images** by clicking on the eye (show) in the left pane.
5. Click on the parameters button and set the parameters. In particular, pay attention to :
5. Click on the parameters button and set the parameters. In particular, pay attention to :
- Set 2D/3D option
-**Set 2D/3D option**.
- Set the width of a PSF. You can also do it by drawing a square around a PSF and by clicking on get_ROI
-**Set the PSF width**. You can also do it by drawing a square around a PSF and by clicking on get_ROI.
- Set the number of EigenPSFs (this can be done afterwards)
-**Set the number of EigenPSFs** (this can be done afterwards as well).
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## Future releases
## Future releases
The current code is based purely on Java. Unfortunately, some operations (convolutions with FFTs) are resource intensive. We plan to:
1. Develop a Pytorch package.
2. Bind the current Java/Fiji interface with this code.
3. Develop a Napari plugin for a better Pytorch integration.
## Contributing
## Contributing
If you want to contribute to this project, do not hesitate to contact the core developing team.
If you want to contribute to this project, do not hesitate to contact the core developing team.
## License
valentin.debarnot@gmail.com
emmanuel.soubies@irit.fr
daniel.sage@epfl.ch
pierre.weiss@cnrs.fr
## References
This work heavily relies on the following references.
V. Debarnot, P. Escande, T. Mangeat and P. Weiss, "Learning Low-Dimensional Models of Microscopes," in IEEE Transactions on Computational Imaging, vol. 7, pp. 178-190, 2021, doi: 10.1109/TCI.2020.3048295.
Debarnot, V., & Weiss, P. (2023). Blind inverse problems with isolated spikes. Information and Inference: A Journal of the IMA, 12(1), 26-71.
Bigot, J., Escande, P., & Weiss, P. (2019). Estimation of linear operators from scattered impulse responses. Applied and Computational Harmonic Analysis, 47(3), 730-758.
