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Pré-Publication, Document De Travail Année : 2024

SPyRiT: an open source package for single-pixel imaging based on deep learning

Résumé

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, we compare several supervised and plug-and-play methods, including post-processing and iterative strategies. Our 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. The modularity of SPyRiT enables the evaluation of various configurations and the rigorous benchmarking of reconstructions based on deep learning in single-pixel imaging, as well as in related fields such as ghost imaging.
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Dates et versions

hal-04662876 , version 1 (26-07-2024)

Identifiants

  • HAL Id : hal-04662876 , version 1

Citer

Juan F P J Abascal, Thomas Baudier, Romain Phan, Audrey Repetti, Nicolas Ducros. SPyRiT: an open source package for single-pixel imaging based on deep learning. 2024. ⟨hal-04662876⟩
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