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3D Gaussian adaptive reconstruction for Fourier light-field microscopy
Chenyu Xu, Zhouyu Jin, Chengkang Shen, Hao Zhu, Zhan Ma, Bo Xiong, You Zhou, Xun Cao, and Ning Gu

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, may face challenges in achieving optimal axial resolution and preserving signal quality due to the inherently ill-posed nature of the inverse problem. While data-driven approaches enhance spatial resolution by leveraging high-quality paired datasets or imposing structural priors, physics-informed self-supervised learning has emerged as a compelling precedent for overcoming these limitations. In this work, 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.

Advanced Imaging
On the CoverSep. 23, 2025, Vol. 2 Issue 5 055001 (2025)
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