High-resolution single-pixel imaging in real time with iterative or deep learning-based reconstruction enhancement
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918133979152384 |
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| author | Pastuszczak, Anna Stojek, Rafał Wróbel, Piotr Cwojdzińska, Magdalena Sobczak, Kacper Kotyński, Rafał |
| author_facet | Pastuszczak, Anna Stojek, Rafał Wróbel, Piotr Cwojdzińska, Magdalena Sobczak, Kacper Kotyński, Rafał |
| contents | We introduce a compressive single-pixel imaging (SPI) framework for high-resolution image capture in fractions of a second. This framework combines a dedicated sampling strategy with a tailored reconstruction method to enable high-quality imaging of spatially sparse scenes at the native 1024x768 resolution of a digital micromirror device (DMD). The reconstruction process consists of two phases: first, the measured data is processed using the generalized inverse of the measurement matrix for quick image recovery. Then, the spatial sparsity of the scene is leveraged to enhance reconstruction in dense areas, using either an iterative method or a neural network-based approach. With a compression ratio of 0.41% and an image acquisition rate of 6.8 Hz at 22 kHz DMD operation, this framework supports real-time, high-resolution dynamic imaging with the reconstruction that matches the acquisition rate on a mid-tier desktop GPU. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01497 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High-resolution single-pixel imaging in real time with iterative or deep learning-based reconstruction enhancement Pastuszczak, Anna Stojek, Rafał Wróbel, Piotr Cwojdzińska, Magdalena Sobczak, Kacper Kotyński, Rafał Image and Video Processing Signal Processing We introduce a compressive single-pixel imaging (SPI) framework for high-resolution image capture in fractions of a second. This framework combines a dedicated sampling strategy with a tailored reconstruction method to enable high-quality imaging of spatially sparse scenes at the native 1024x768 resolution of a digital micromirror device (DMD). The reconstruction process consists of two phases: first, the measured data is processed using the generalized inverse of the measurement matrix for quick image recovery. Then, the spatial sparsity of the scene is leveraged to enhance reconstruction in dense areas, using either an iterative method or a neural network-based approach. With a compression ratio of 0.41% and an image acquisition rate of 6.8 Hz at 22 kHz DMD operation, this framework supports real-time, high-resolution dynamic imaging with the reconstruction that matches the acquisition rate on a mid-tier desktop GPU. |
| title | High-resolution single-pixel imaging in real time with iterative or deep learning-based reconstruction enhancement |
| topic | Image and Video Processing Signal Processing |
| url | https://arxiv.org/abs/2509.01497 |