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CellINR: Implicitly Overcoming Photo-induced Artifacts in 4D Live Fluorescence Microscopy

arXiv 2508.19300 ↗First posted 25 Aug 2025eess.IV cs.AI cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 65 references that are in the map. First posted on arXiv 25 Aug 2025.

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27 of 65 references.

References · 27

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15 more
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