Une de plus en bonus ;)

@article{Abascal_2025, title = {{{SPyRiT}} 3.0: An Open Source Package for Single-Pixel Imaging Based on Deep Learning}, shorttitle = {{{SPyRiT}} 3.0}, author = {Abascal, J. and Baudier, T. and Phan, R. and Repetti, A. and Ducros, N.}, year = 2025, month = jun, journal = {Optics Express}, volume = {33}, number = {13}, pages = {27988--28005}, publisher = {Optica Publishing Group}, issn = {1094-4087}, doi = {10.1364/OE.559227}, urldate = {2025-07-14}, abstract = {Single-pixel imaging is able to acquire an image from a few point measurements thanks to dedicated reconstruction algorithms. In recent years, reconstruction approaches based on deep learning have outperformed most alternatives. However, computational experiments and data-driven methods have become difficult, if not impossible, to reproduce. The development of tools enabling reproducibility and benchmarking is therefore now essential. This paper describes SPyRiT, an open-source PyTorch-based toolbox capable of handling various simulation configurations and reconstruction methods based on deep learning. In particular, SPyRiT implements both existing and new supervised and plug-and-play methods, including post-processing and iterative strategies. Our reconstruction results demonstrate that supervised methods trained on simulated data can be successfully applied to experimental data when the signal-to-noise ratio of the measurements is higher or equal to that of the training phase. On the other hand, the hyperparameter of the plug-and-play methods can be tuned to manage lower signal-to-noise ratios. Among the supervised methods, DC-Net is found to be robust to deviations in the noise level, achieving similar results to plug-and-play methods without hyperparameter selection, while offering low memory requirements and short reconstruction times. The modularity of SPyRiT enables the rigorous benchmarking of reconstructions based on deep learning in single-pixel imaging, as well as in related fields such as ghost imaging. Thanks to its modularity and versatility, SPyRiT is suitable for further studies beyond this work and could also benefit other modalities in the field of computational optics.}, langid = {english}, keywords = {Computational imaging,Deep learning,Fluorescence lifetime imaging,Ghost imaging,Magnetic resonance imaging,Single pixel imaging}, file = {C:\Users\ducros\Zotero\storage\DZKXQLJL\Abascal et al. - 2025 - SPyRiT 3.0 an open source package for single-pixel imaging based on deep learning.pdf} }

Plutot celle-ci :

@article{BenetiMartins_2023, title = {{{OpenSpyrit}}: An Ecosystem for Open Single-Pixel Hyperspectral Imaging}, shorttitle = {{{OpenSpyrit}}}, author = {Beneti Martins, Guilherme and {Mahieu-Williame}, Laurent and Baudier, Thomas and Ducros, Nicolas}, year = 2023, month = may, journal = {Optics Express}, volume = {31}, number = {10}, pages = {15599}, issn = {1094-4087}, doi = {10.1364/OE.483937}, urldate = {2023-08-28}, abstract = {This paper describes OpenSpyrit, an open access and open source ecosystem for reproducible research in hyperspectral single-pixel imaging, composed of SPAS (a Python single-pixel acquisition software), SPYRIT (a Python single-pixel reconstruction toolkit) and SPIHIM (a single-pixel hyperspectral image collection). The proposed OpenSpyrit ecosystem responds to the need for reproducibility and benchmarking in single-pixel imaging by providing open data and open software. The SPIHIM collection, which is the first open-access FAIR dataset for hyperspectral single-pixel imaging, currently includes 140 raw measurements acquired using SPAS and the corresponding hypercubes reconstructed using SPYRIT. The hypercubes are reconstructed by both inverse Hadamard transformation of the raw data and using the denoised completion network (DC-Net), a data-driven reconstruction algorithm. The hypercubes obtained by inverse Hadamard transformation have a native size of 64\,\texttimes\,64\,\texttimes\,2048 for a spectral resolution of 2.3 nm and a spatial resolution that is comprised between 182.4 \textmu m and 15.2 \textmu m depending on the digital zoom. The hypercubes obtained using the DC-Net are reconstructed at an increased resolution of 128\,\texttimes\,128\,\texttimes\,2048. The OpenSpyrit ecosystem should constitute a reference to support benchmarking for future developments in single-pixel imaging.}, langid = {english}, file = {C\:\Users\ducros\Zotero\storage\NDYHKKKJ\Beneti Martins et al. - 2023 - OpenSpyrit an ecosystem for open single-pixel hyp.pdf;C\:\Users\ducros\Zotero\storage\PW824TZX\Beneti Martins et al. - 2023 - OpenSpyrit an ecosystem for open single-pixel hyperspectral imaging.pdf} }

See [Harwit_1979, section 3.5.4]

@book{Harwit_1979, title = {Hadamard {{Transform Optics}}}, author = {Harwit, Martin and Sloane, Neil J. A.}, year = 1979, publisher = {Academic Press}, isbn = {978-0-12-330050-8 0-12-330050-9} }