Single Pixel Imaging and Compressive Sensing: A Practical Tutorial
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908780048941056 |
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| author | Scheidt, Dennis |
| author_facet | Scheidt, Dennis |
| contents | Single Pixel Imaging is an emerging imaging technique that employs a bucket detector (photodiode) to sample a spatially modulated light field, rather than measuring the spatial distribution with an array of detectors. This approach provides a low-cost alternative for imaging at unconventional wavelengths and enables improved signal collection in noisy measurement environments. Furthermore, it allows the application of compressive sensing to reduce the amount of acquired data and measurement time, facilitating live or in vivo imaging applications. This tutorial presents the experimental implementation of measurement bases and compressive sensing reconstruction methods, including both deterministic algorithms and deep learning approaches. Accompanying Python notebooks guide readers through the reproduction of the presented results and support the application of the methods to their own work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_15248 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Single Pixel Imaging and Compressive Sensing: A Practical Tutorial Scheidt, Dennis Optics Data Analysis, Statistics and Probability 0078 Single Pixel Imaging is an emerging imaging technique that employs a bucket detector (photodiode) to sample a spatially modulated light field, rather than measuring the spatial distribution with an array of detectors. This approach provides a low-cost alternative for imaging at unconventional wavelengths and enables improved signal collection in noisy measurement environments. Furthermore, it allows the application of compressive sensing to reduce the amount of acquired data and measurement time, facilitating live or in vivo imaging applications. This tutorial presents the experimental implementation of measurement bases and compressive sensing reconstruction methods, including both deterministic algorithms and deep learning approaches. Accompanying Python notebooks guide readers through the reproduction of the presented results and support the application of the methods to their own work. |
| title | Single Pixel Imaging and Compressive Sensing: A Practical Tutorial |
| topic | Optics Data Analysis, Statistics and Probability 0078 |
| url | https://arxiv.org/abs/2601.15248 |