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.

翻译：4D活体荧光显微成像常因长时间高强度照明而受损，这种照明会引发光漂白和光毒性效应，从而产生光诱导伪影，严重损害图像连续性与细节恢复。为应对这一挑战，我们提出CellINR框架——一种基于隐式神经表示的案例特异性优化方法。该方法采用盲卷积与结构增强策略，将三维空间坐标映射至高频域，从而实现对细胞结构的精确建模与高精度重建，同时有效区分真实信号与伪影。实验结果表明，CellINR在伪影消除与结构连续性恢复方面显著优于现有技术，并首次提供了用于评估重建性能的配对4D活细胞成像数据集，为后续定量分析与生物学研究奠定了坚实基础。代码与数据集将公开。