Letter|2 Article(s)
3D Gaussian adaptive reconstruction for Fourier light-field microscopy|On the Cover
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. 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
- Publication Date: Sep. 23, 2025
- Vol. 2, Issue 5, 055001 (2025)
Ultrahigh-speed schlieren photography via diffraction-gated real-time mapping|On the Cover
Xianglei Liu, Patrick Kilcullen, Youmin Wang, Brandon Helfield, and Jinyang Liang
Single-shot ultrahigh-speed mapping photography is essential for analyzing fast dynamic processes across various scientific disciplines. Among available techniques, optical diffraction has recently been implemented as a nanosecond time gate for mapping photography. Despite attractive features in light throughput and cost efficiency, existing systems in this approach can sense only light intensity with limited sequence depth and imaging speed. To overcome these limitations, we develop diffraction-gated real-time ultrahigh-speed mapping schlieren (DRUMS) photography. Using a digital micromirror device as a coded dynamic two-dimensional blazed grating, DRUMS photography can record schlieren images of transient events in real time at an imaging speed of 9.8 million frames per second and a sequence depth of 13 frames. We present the working principle of DRUMS photography in both theoretical derivation and numerical simulation, and we apply DRUMS photography to the single-shot real-time video recording of laser-induced breakdown in water. Single-shot ultrahigh-speed mapping photography is essential for analyzing fast dynamic processes across various scientific disciplines. Among available techniques, optical diffraction has recently been implemented as a nanosecond time gate for mapping photography. Despite attractive features in light throughput and cost efficiency, existing systems in this approach can sense only light intensity with limited sequence depth and imaging speed. To overcome these limitations, we develop diffraction-gated real-time ultrahigh-speed mapping schlieren (DRUMS) photography. Using a digital micromirror device as a coded dynamic two-dimensional blazed grating, DRUMS photography can record schlieren images of transient events in real time at an imaging speed of 9.8 million frames per second and a sequence depth of 13 frames. We present the working principle of DRUMS photography in both theoretical derivation and numerical simulation, and we apply DRUMS photography to the single-shot real-time video recording of laser-induced breakdown in water.
Advanced Imaging
- Publication Date: Feb. 27, 2025
- Vol. 2, Issue 1, 015001 (2025)
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