Deep Learning Empowered Sub-Diffraction Terahertz Backpropagation Single-Pixel Imaging
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909717772632064 |
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| author | Zhu, Yongsheng Liu, Shaojing Wang, Ximiao Li, Runli Yang, Haili Wang, Jiali Zhu, Hongjia Ke, Yanlin Xu, Ningsheng Chen, Huanjun Deng, Shaozhi |
| author_facet | Zhu, Yongsheng Liu, Shaojing Wang, Ximiao Li, Runli Yang, Haili Wang, Jiali Zhu, Hongjia Ke, Yanlin Xu, Ningsheng Chen, Huanjun Deng, Shaozhi |
| contents | Terahertz single-pixel imaging (THz SPI) has garnered widespread attention for its potential to overcome challenges associated with THz focal plane arrays. However, the inherently long wavelength of THz waves limits imaging resolution, while achieving subwavelength resolution requires harsh experimental conditions and time-consuming processes. Here, we propose a sub-diffraction THz backpropagation SPI technique. We illuminate the object with continuous-wave 0.36-THz radiation (λ0 = 833.3 μm). The transmitted THz wave is modulated by prearranged patterns generated on a 500-μm-thick silicon wafer and subsequently recorded by a far-field single-pixel detector. An untrained neural network constrained with the physical SPI process iteratively reconstructs the THz images with an ultralow sampling ratio of 1.5625%, significantly reducing the long sampling times. To further suppress the THz diffraction-field effects, a backpropagation SPI from near field to far field is implemented by integrating with a THz physical propagation model into the output layer of the network. Notably, using the thick wafer where THz evanescent field cannot be fully recorded, we achieve a spatial resolution of 118 μm (~λ0/7) through backpropagation SPI, thus eliminating the need for ultrathin photomodulators. This approach provides an efficient solution for advancing THz microscopic imaging and addressing other inverse imaging challenges. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07839 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Learning Empowered Sub-Diffraction Terahertz Backpropagation Single-Pixel Imaging Zhu, Yongsheng Liu, Shaojing Wang, Ximiao Li, Runli Yang, Haili Wang, Jiali Zhu, Hongjia Ke, Yanlin Xu, Ningsheng Chen, Huanjun Deng, Shaozhi Image and Video Processing Artificial Intelligence Terahertz single-pixel imaging (THz SPI) has garnered widespread attention for its potential to overcome challenges associated with THz focal plane arrays. However, the inherently long wavelength of THz waves limits imaging resolution, while achieving subwavelength resolution requires harsh experimental conditions and time-consuming processes. Here, we propose a sub-diffraction THz backpropagation SPI technique. We illuminate the object with continuous-wave 0.36-THz radiation (λ0 = 833.3 μm). The transmitted THz wave is modulated by prearranged patterns generated on a 500-μm-thick silicon wafer and subsequently recorded by a far-field single-pixel detector. An untrained neural network constrained with the physical SPI process iteratively reconstructs the THz images with an ultralow sampling ratio of 1.5625%, significantly reducing the long sampling times. To further suppress the THz diffraction-field effects, a backpropagation SPI from near field to far field is implemented by integrating with a THz physical propagation model into the output layer of the network. Notably, using the thick wafer where THz evanescent field cannot be fully recorded, we achieve a spatial resolution of 118 μm (~λ0/7) through backpropagation SPI, thus eliminating the need for ultrathin photomodulators. This approach provides an efficient solution for advancing THz microscopic imaging and addressing other inverse imaging challenges. |
| title | Deep Learning Empowered Sub-Diffraction Terahertz Backpropagation Single-Pixel Imaging |
| topic | Image and Video Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2505.07839 |