用隐式神经表示修复荧光显微中光照损伤，提升活细胞成像清晰度。
CellINR: Implicitly Overcoming Photo-induced Artifacts in 4D Live Fluorescence Microscopy
- 基于隐式神经表征，通过盲卷积与结构增强建模3D空间高频特征。
- 显著减少光漂白伪影，恢复细胞结构连续性，优于现有方法。
- 首次提供成对4D活细胞成像数据集，适合生物成像与重建研究者。
4D活细胞荧光显微常因长时间高强度光照导致光漂白和光毒性，引发光诱导伪影，严重破坏图像连续性和细节恢复。为此，本文提出CellINR框架，一种基于隐式神经表示的特定案例优化方法。该方法采用盲卷积与结构增强策略，将3D空间坐标映射至高频域，实现对细胞结构的精准建模与高精度重建，有效区分真实信号与伪影。实验表明，CellINR在伪影去除与结构连续性恢复方面显著优于现有技术，并首次发布用于评估重建性能的成对4D活细胞成像数据集，为后续定量分析与生物学研究奠定基础。代码与数据集将公开。
原文摘要 · Abstract (English)
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.
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