Fractional optical vortices in the terahertz regime are supposed to have unique applications in various areas, i.e., terahertz communications, optical manipulations, and terahertz imaging. Howev

Fractional optical vortices in the terahertz regime are supposed to have unique applications in various areas, i.e., terahertz communications, optical manipulations, and terahertz imaging. However, it is still challenging to generate and manipulate high power terahertz vortices. Here we present a way to generate intense terahertz vortex beams with continuously tunable topological charge by injecting a weakly-relativistic ultrashort laser pulse into a parabolic plasma channel. By adjusting the injection conditions of the laser pulse, the trajectory of the laser centroid can be twisted into a cylindrical spiral, along which laser wakefields are also excited. Due to the inhomogeneous transverse density profile of the plasma channel and laser wakefield excitation, intense terahertz radiation carrying orbital angular momentum is produced with field strength reaching sub GV/m, even though the drive laser energy is at a few tens of mJ. The topological charge of such a radiation is determined by the laser trajectories, which is continuously tunable as demonstrated by theoretical analysis as well as three-dimensional particle-in-cell simulations. Such terahertz vortices with unique properties may find applications in broad areas.show less

Spatiotemporal optical vortices (STOVs) have attracted significant attention for their unique properties. Recently, the second harmonic generation (SHG) of STOV pulses has been experimentally de

Spatiotemporal optical vortices (STOVs) have attracted significant attention for their unique properties. Recently, the second harmonic generation (SHG) of STOV pulses has been experimentally demonstrated [Nat. Photon. 15(8), 608–613 (2021); Optica 8(5), 594 (2021)], but the phase singularity dynamics during this process remain elusive. Here, we theoretically investigate the separation and tilting of phase singularities in STOVs during SHG. Using the nonlinear Maxwell equation, we show that singularity separation is governed by group velocity mismatch, with accurate predictions provided by a Simpson-type integral under weak spatiotemporal walk-off conditions. Additionally, paraxial wave equation analysis reveals that propagation induces singularity tilting, driven by spatial phase shifts. Our results not only offer deeper insights into the spatiotemporal coupling induced by complex nonlinear interactions, but also underlie the physical mechanisms of frequency up-conversion in space-time light pulses.show less

Light-field microscopy (LFM) enables single-shot 3D imaging by capturing both spatial and angular data. However, limited resolution drove the emergence of scanning LFM (sLFM). By merging digital

Light-field microscopy (LFM) enables single-shot 3D imaging by capturing both spatial and angular data. However, limited resolution drove the emergence of scanning LFM (sLFM). By merging digital adaptive optics with dense periodic scanning, sLFM achieves near-diffraction-limited resolution, wide volumetric coverage and low phototoxicity. We discuss its breakthroughs in rapid neural imaging, migrasome biology and future ML-driven, label-free expansions.show less

The current state of traditional optoelectronic imaging technology is constrained by the inherent limitations of its hardware. These limitations pose significant challenges in acquiring higher-d

The current state of traditional optoelectronic imaging technology is constrained by the inherent limitations of its hardware. These limitations pose significant challenges in acquiring higher-dimensional information and reconstructing accurate images, particularly in applications such as scattering imaging, super-resolution, and complex scene reconstruction. However, the rapid development and widespread adoption of deep learning are reshaping the field of optical imaging through computational imaging technology. Data-driven computational imaging has ushered in a paradigm shift by leveraging the nonlinear expression and feature learning capabilities of neural networks. This approach transcends the limitations of conventional physical models, enabling the adaptive extraction of critical features directly from data. As a result, computational imaging overcomes the traditional "what you see is what you get" paradigm, paving the way for more compact optical system designs, broader information acquisition, and improved image reconstruction accuracy. These advancements have significantly enhanced the interpretation of high-dimensional light-field information and the processing of complex images. This paper presents a comprehensive analysis of the integration of deep learning and computational imaging, emphasizing its transformative potential in three core areas: computational optical system design, high-dimensional information interpretation, and image enhancement and processing. Additionally, it addresses the challenges and future directions of this cutting-edge technology, providing novel insights into interdisciplinary imaging research.show less