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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.19300 |
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| _version_ | 1866914332324921344 |
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| author | Zhao, Cunmin Luo, Ziyuan Guan, Guoye Li, Zelin Ma, Yiming Zhao, Zhongying Wan, Renjie |
| author_facet | Zhao, Cunmin Luo, Ziyuan Guan, Guoye Li, Zelin Ma, Yiming Zhao, Zhongying Wan, Renjie |
| contents | 4D live fluorescence microscopy is often compromised by prolonged high intensity illumination which induces photobleaching and phototoxic effects that generate photo-induced artifacts and severely impair image continuity and detail recovery. To address this challenge, we propose the CellINR framework, a case-specific optimization approach based on implicit neural representation. The method employs blind convolution and structure amplification strategies to map 3D spatial coordinates into the high frequency domain, enabling precise modeling and high-accuracy reconstruction of cellular structures while effectively distinguishing true signals from artifacts. Experimental results demonstrate that CellINR significantly outperforms existing techniques in artifact removal and restoration of structural continuity, and for the first time, a paired 4D live cell imaging dataset is provided for evaluating reconstruction performance, thereby offering a solid foundation for subsequent quantitative analyses and biological research. The code and dataset will be public. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19300 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CellINR: Implicitly Overcoming Photo-induced Artifacts in 4D Live Fluorescence Microscopy Zhao, Cunmin Luo, Ziyuan Guan, Guoye Li, Zelin Ma, Yiming Zhao, Zhongying Wan, Renjie Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 32H10 F.2.2; I.2.7 4D live fluorescence microscopy is often compromised by prolonged high intensity illumination which induces photobleaching and phototoxic effects that generate photo-induced artifacts and severely impair image continuity and detail recovery. To address this challenge, we propose the CellINR framework, a case-specific optimization approach based on implicit neural representation. The method employs blind convolution and structure amplification strategies to map 3D spatial coordinates into the high frequency domain, enabling precise modeling and high-accuracy reconstruction of cellular structures while effectively distinguishing true signals from artifacts. Experimental results demonstrate that CellINR significantly outperforms existing techniques in artifact removal and restoration of structural continuity, and for the first time, a paired 4D live cell imaging dataset is provided for evaluating reconstruction performance, thereby offering a solid foundation for subsequent quantitative analyses and biological research. The code and dataset will be public. |
| title | CellINR: Implicitly Overcoming Photo-induced Artifacts in 4D Live Fluorescence Microscopy |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 32H10 F.2.2; I.2.7 |
| url | https://arxiv.org/abs/2508.19300 |