Super Depth-of-Field Snapshot Light-Field Microscopy

Snapshot light-field microscopes (SLFMs) enable high-speed 3D observation (4D imaging) of dynamic micro-targets. However, their performance is fundamentally restricted by the inherent planar structure of microlens arrays (MLAs), which leads to tradeoffs among resolution, depth of field (DOF), and depth perception, making large DOF light-field detection extremely challenging. Here, we propose a paradigm in curved MLA with a logarithmic profile, featuring extreme depth of focus (>346λ), enhanced parallax (273%), and near-diffraction-limited resolution, outperforming conventional planar counterparts. Integrated into a commercial microscope and combined with neural network-based reconstruction, this architecture yields a super depth-of-field snapshot light-field microscope (SDOF-SLFM), achieving over 15 times improvement in DOF (>3 mm) and enabling in situ stereo imaging of micro-pyramids and 4D tracking of micro-particles in flow fields. This study offers a practical pathway for upgrading SLFMs without complex system assembly or sample processing, facilitating the adaptation of conventional commercial microscopes to dynamic 4D imaging applications such as biological laboratories and microfluidic flow monitoring. © 2026 Wiley-VCH GmbH.