Abstract
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.
Abstract (translated)
四维活体荧光显微成像经常因长时间高强度照明而受到损害，这种照明会导致光漂白和光毒性效应，从而产生光诱导的伪影，并严重阻碍图像连续性和细节恢复。为了解决这一挑战，我们提出了基于隐式神经表示的特定案例优化方法——CellINR框架。该方法采用盲卷积和结构增强策略，将三维空间坐标映射到高频域中，能够精确建模并以高精度重建细胞结构，同时有效区分真实信号与伪影。实验结果表明，CellINR在去除伪影和恢复结构连续性方面显著优于现有技术，并且首次提供了一对四维活体细胞成像数据集用于评估重建性能，从而为后续的定量分析和生物研究奠定了坚实的基础。代码和数据集将会公开发布。
URL
https://arxiv.org/abs/2508.19300
