用3D高斯点云提升光场显微镜的三维成像质量
3D Gaussian Adaptive Reconstruction for Fourier Light-Field Microscopy
- 基于3D高斯点云的自监督学习框架，无需配对数据
- 相比传统方法，轴向分辨率显著提升，重建更清晰
- 计算高效，适合实时三维光学显微成像应用
与传统光场显微镜（LFM）相比，傅里叶光场显微镜（FLFM）在孔径平面上引入子孔径分割，实现空间不变采样，提升了空间分辨率。然而，传统FLFM重建方法（如Richardson-Lucy反卷积）因逆问题病态性导致轴向分辨率差且信号退化。尽管数据驱动方法通过高质量配对数据或结构先验提升空间分辨率，但基于神经辐射场（NeRF）的方法虽采用物理信息自监督学习克服缺陷，却面临高昂的计算成本和内存开销。为此，我们提出3D高斯自适应断层扫描（3DGAT），一种基于3D高斯点云的自监督学习框架，显著提升FLFM的体素重建质量，同时保持计算效率。实验表明，该方法在分辨率和重建精度上均优于现有方法，展现出推动FLFM成像技术发展的潜力。
原文摘要 · Abstract (English)
Compared to light-field microscopy (LFM), which enables high-speed volumetric imaging but suffers from non-uniform spatial sampling, Fourier light-field microscopy (FLFM) introduces sub-aperture division at the pupil plane, thereby ensuring spatially invariant sampling and enhancing spatial resolution. Conventional FLFM reconstruction methods, such as Richardson-Lucy (RL) deconvolution, exhibit poor axial resolution and signal degradation due to the ill-posed nature of the inverse problem. While data-driven approaches enhance spatial resolution by leveraging high-quality paired datasets or imposing structural priors, Neural Radiance Fields (NeRF)-based methods employ physics-informed self-supervised learning to overcome these limitations, yet they are hindered by substantial computational costs and memory demands. Therefore, we propose 3D Gaussian Adaptive Tomography (3DGAT) for FLFM, a 3D gaussian splatting based self-supervised learning framework that significantly improves the volumetric reconstruction quality of FLFM while maintaining computational efficiency. Experimental results indicate that our approach achieves higher resolution and improved reconstruction accuracy, highlighting its potential to advance FLFM imaging and broaden its applications in 3D optical microscopy.
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