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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Accès en ligne: | https://arxiv.org/abs/2507.10129 |
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| _version_ | 1866908448823705600 |
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| author | Liu, Yongtao Wu, Tianxiao Zhou, Xiao Wang, Fan |
| author_facet | Liu, Yongtao Wu, Tianxiao Zhou, Xiao Wang, Fan |
| contents | Single-beam scanning microscopy (SBSM) is one of the most robust strategies for commercial optical systems. Although structured illumination combined with Fourier-domain spatial spectrum fusion can enhance SBSM resolution beyond the diffraction limit, a sophisticated detection system is still required to optimize both effective resolution and signal-to-noise ratio.Here, we report that the diverse nonlinear responses of upconversion nanoparticles can unlock a new mode of Computational Progressively Emission Saturated Nanoscopy (CPSN), which employs a single doughnut-shaped excitation beam assisted by deep learning to simplify conventional microscopy. By modulating the excitation power, the smooth transition of the point spread function (PSF) from doughnut-shaped to Gaussian can be achieved, allowing for accessing different spatial frequency components of the sample. Then, in order to enhance time resolution, the doughnut-shaped beam at low power and the saturated Gaussian-like image were predicted by the doughnut-shaped beam at low saturation threshold based on the power dependence curve. Furthermore, a deep recursive residual network (DRRN) is employed to fusion these progressively complementary spatial frequency information into a final super-resolved image that encompasses the full frequency wwinformation. This approach can achieve high-quality super-resolution imaging with a spatial resolution of 33 nm, corresponding to 1/29th of the excitation wavelength, 55 dB of SNR ratio contracted to 7 dB in Gaussian imaging and applicable to any wavelength. The unique combination of nonlinear saturation and deep learning computational reconstruction could open a new avenue for simplifying the optical system and enhancing imaging quality in single-beam super-resolution nanoscopy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_10129 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Nonlinear Spectral Fusion Super-Resolution Fluorescence Microscopy based on Progressively Saturated Upconversion Nanoparticles Liu, Yongtao Wu, Tianxiao Zhou, Xiao Wang, Fan Optics Single-beam scanning microscopy (SBSM) is one of the most robust strategies for commercial optical systems. Although structured illumination combined with Fourier-domain spatial spectrum fusion can enhance SBSM resolution beyond the diffraction limit, a sophisticated detection system is still required to optimize both effective resolution and signal-to-noise ratio.Here, we report that the diverse nonlinear responses of upconversion nanoparticles can unlock a new mode of Computational Progressively Emission Saturated Nanoscopy (CPSN), which employs a single doughnut-shaped excitation beam assisted by deep learning to simplify conventional microscopy. By modulating the excitation power, the smooth transition of the point spread function (PSF) from doughnut-shaped to Gaussian can be achieved, allowing for accessing different spatial frequency components of the sample. Then, in order to enhance time resolution, the doughnut-shaped beam at low power and the saturated Gaussian-like image were predicted by the doughnut-shaped beam at low saturation threshold based on the power dependence curve. Furthermore, a deep recursive residual network (DRRN) is employed to fusion these progressively complementary spatial frequency information into a final super-resolved image that encompasses the full frequency wwinformation. This approach can achieve high-quality super-resolution imaging with a spatial resolution of 33 nm, corresponding to 1/29th of the excitation wavelength, 55 dB of SNR ratio contracted to 7 dB in Gaussian imaging and applicable to any wavelength. The unique combination of nonlinear saturation and deep learning computational reconstruction could open a new avenue for simplifying the optical system and enhancing imaging quality in single-beam super-resolution nanoscopy. |
| title | Nonlinear Spectral Fusion Super-Resolution Fluorescence Microscopy based on Progressively Saturated Upconversion Nanoparticles |
| topic | Optics |
| url | https://arxiv.org/abs/2507.10129 |