3D Gaussian Adaptive Reconstruction for Fourier Light-Field Microscopy

TL;DR

This paper introduces 3D Gaussian Adaptive Reconstruction (3DGAT), a self-supervised learning method that enhances Fourier light-field microscopy's volumetric imaging quality while maintaining computational efficiency.

Contribution

The paper presents 3DGAT, a novel 3D gaussian splatting framework that improves FLFM reconstruction quality without high computational costs, addressing limitations of existing methods.

Findings

Achieves higher resolution in FLFM reconstructions

Improves accuracy of volumetric imaging

Maintains computational efficiency

Abstract

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.