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Lihui Meng

Publications and source records attributed to Lihui Meng.

2 recordsLinked to original sources

6.2-GW tabletop attosecond light source

The generation of attosecond pulses (1 as=10-18 s) has enabled real-time observation and manipulation of coherent electron dynamics, yet their low peak power has hindered the development of advanced attosecond pump-probe spectroscopy and attosecond nonlinear metrology. Here we overcome this limitation by generating 1.64 uJ, 263 as isolated attosecond pulses with a peak power of 6.2 GW, the highest pulse energy and peak power reported for a tabletop isolated attosecond source. This is achieved by combining a 2.1 TW, few-cycle (8.3 fs) two-color synthesizer with a loose focusing geometry that enables macroscopic phase-matching. The synthesizer features a stabilized carrier-envelope phase and an actively synchronized relative time delay between the two-color channels, ensuring high stability and reproducibility. This robust tabletop attosecond source enables nonlinear effect experiments that were previously inaccessible with lower-power IAPs, establishing a foundation for advanced attosecond spectroscopy and nonlinear metrology.

physics.optics

O-PRESS: Boosting OCT axial resolution with Prior guidance, Recurrence, and Equivariant Self-Supervision

Optical coherence tomography (OCT) is a noninvasive technology that enables real-time imaging of tissue microanatomies. The axial resolution of OCT is intrinsically constrained by the spectral bandwidth of the employed light source while maintaining a fixed center wavelength for a specific application. Physically extending this bandwidth faces strong limitations and requires a substantial cost. We present a novel computational approach, called as O-PRESS, for boosting the axial resolution of OCT with Prior Guidance, a Recurrent mechanism, and Equivariant Self-Supervision. Diverging from conventional superresolution methods that rely on physical models or data-driven techniques, our method seamlessly integrates OCT modeling and deep learning, enabling us to achieve real-time axial-resolution enhancement exclusively from measurements without a need for paired images. Our approach solves two primary tasks of resolution enhancement and noise reduction with one treatment. Both tasks are executed in a self-supervised manner, with equivariance imaging and free space priors guiding their respective processes. Experimental evaluations, encompassing both quantitative metrics and visual assessments, consistently verify the efficacy and superiority of our approach, which exhibits performance on par with fully supervised methods. Importantly, the robustness of our model is affirmed, showcasing its dual capability to enhance axial resolution while concurrently improving the signal-to-noise ratio.

eess.IV