arXiv subjects

Li-Yu Yu

Publications and source records attributed to Li-Yu Yu.

7 recordsLinked to original sources

arXiv2026

Tunable supercontinuum in multimode fiber via bending-induced dispersion modification

Nonlinear pulse propagation in multimode fibers (MMFs) offers a compact, low-cost route to broadband, tunable femtosecond light, but most control schemes act by changing the spatial mode composition, typically resulting in irregular or speckled beams in exchange for maximal spectral tunability. Here we introduce a complementary mechanism: bending-induced local dispersion modification of a high-order mode (HOM) to steer the spectrum while keeping the spatial mode fixed. We launch an LP0,7 mode into a step-index MMF and apply programmable macrobends near the input. With a standard Yb pump at 1030 nm, this yields spatially clean, continuous spectral tuning across 700-1350 nm, while the output profile remains Bessel-like and robust to reconfiguration of controlled bends. A perturbative model explains the observed spatial-spectral decorrelation, showing that moderate curvature produces first- and second-order shifts in group delay and group-velocity dispersion of the HOM with minimal change in its modal composition; these dispersion shifts control soliton fission, dispersive-wave emission, and the soliton self-frequency shift. We further validate application utility by driving multicolor, extended-depth-of-focus multiphoton microscopy directly from this all-fiber source. To our knowledge, this is the first demonstration of bending-induced dispersion modification, rather than mode mixing, used to tune MMF supercontinuum spectra without sacrificing beam quality, laying the foundation for an alternative pathway to tunable femtosecond illumination for imaging and spectroscopy.

physics.optics↗

arXiv2025

Self-localized ultrafast pencil beam for volumetric multiphoton imaging

The formation of organized optical states in multidimensional systems is crucial for understanding light-matter interaction and advancing light-shaping technologies. Here, we report the observation of a self-localized, ultrafast pencil beam near the critical power in a standard multimode fiber (MMF) and demonstrate its application in volumetric multiphoton imaging. We show that self-focusing in step-index MMFs, traditionally considered detrimental, can facilitate the formation of a nonlinear spatiotemporal localized state with a sidelobe-suppressed Bessel-like beam profile, exhibiting markedly improved stability and noise characteristics. By simply launching an overfilled on-axis Gaussian beam into a standard MMF, a high-quality ultrafast pencil beam can be generated through a self-localized process and readily integrated into an existing multiphoton point-scanning microscope. We apply this self-localized pencil beam to two-photon imaging of intact mouse enteric nervous systems, benchmarking with diffraction-limited Gaussian beams and outperforming conventional Bessel beams with reduced sidelobes and enhanced resilience to tissue-induced aberration. Finally, we monitor the transferrin uptake dynamics in a live human blood-brain barrier model by combining NAD(P)H-FAD-based metabolic phenotyping with minute-resolved 3D scans, revealing spatially and temporally resolved inter- and intra-cell heterogeneity. Our findings provide new insights into nonlinear dynamics of multidimensional optical systems and offer a promising approach for generating robust ultrafast pencil beams, enabling high-throughput 3D biosystem imaging to elucidate biological transport pathways and guide the design of therapeutics requiring cell-specific delivery.

physics.optics↗

arXiv2024

Deep and Dynamic Metabolic and Structural Imaging in Living Tissues

Label-free imaging through two-photon autofluorescence (2PAF) of NAD(P)H allows for non-destructive and high-resolution visualization of cellular activities in living systems. However, its application to thick tissues and organoids has been restricted by its limited penetration depth within 300 $\mu$m, largely due to tissue scattering at the typical excitation wavelength (~750 nm) required for NAD(P)H. Here, we demonstrate that the imaging depth for NAD(P)H can be extended to over 700 $\mu$m in living engineered human multicellular microtissues by adopting multimode fiber (MMF)-based low-repetition-rate high-peak-power three-photon (3P) excitation of NAD(P)H at 1100 nm. This is achieved by having over 0.5 MW peak power at the band of 1100$\pm$25 nm through adaptively modulating multimodal nonlinear pulse propagation with a compact fiber shaper. Moreover, the 8-fold increase in pulse energy at 1100 nm enables faster imaging of monocyte behaviors in the living multicellular models. These results represent a significant advance for deep and dynamic metabolic and structural imaging of intact living biosystems. The modular design (MMF with a slip-on fiber shaper) is anticipated to allow wide adoption of this methodology for demanding in vivo and in vitro imaging applications, including cancer research, autoimmune diseases, and tissue engineering.

physics.optics↗

arXiv2023

Spectral-temporal-spatial customization via modulating multimodal nonlinear pulse propagation

Multimode fibers (MMFs) have recently reemerged as attractive avenues for nonlinear effects due to their high-dimensional spatiotemporal nonlinear dynamics and scalability for high power. High-brightness MMF sources with effective control of the nonlinear processes would offer new possibilities for a wide range of applications from high-power fiber lasers, to bioimaging and chemical sensing, and to novel physics phenomena. Here we present a simple yet effective way of controlling nonlinear effects at high peak power levels: by leveraging not only the spatial but also the temporal degrees of freedom of the multimodal nonlinear pulse propagation in step-index MMFs using a programmable fiber shaper. This method represents the first method that enables modulation and optimization of multimodal nonlinear pulse propagation, achieving high tunability and broadband high peak power. Its potential as a nonlinear imaging source is further demonstrated by applying the MMF source to multiphoton microscopy, where widely tunable two-photon and three-photon imaging is achieved with adaptive optimization. These demonstrations highlight the effectiveness of directly modulating multimodal nonlinear pulse propagation to enhance the high-dimensional customization and optimize the high spectral brightness of MMF output. These advancements provide new possibilities for technology advances in nonlinear optics, bioimaging, spectroscopy, optical computing, and material processing.

physics.optics↗

arXiv2023

Roadmap on Deep Learning for Microscopy

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences.

physics.optics↗

arXiv2023

High-Fidelity and High-Speed Wavefront Shaping by Leveraging Complex Media

Achieving high-precision light manipulation is crucial for delivering information through complex media with high fidelity. However, existing spatial light modulation devices face a fundamental tradeoff between speed and accuracy. Digital micromirror devices (DMDs) have emerged as a promising candidate as accessible high-speed wavefront shaping devices but at the cost of compromised fidelity, largely due to the limited control degrees of freedom and the challenge of numerically optimizing a binary amplitude mask. Here we leverage the sparse-to-random transformation through complex media to overcome the dimensionality limitation of spatial light modulation devices. We demonstrate that pattern compression in the form of sparsity-constrained wavefront optimization allows sparse and robust wavefront representations of generic patterns in the random basis provided by the complex media, and thus effectively addresses the dimensionality limitation of DMDs, which significantly improves the projection fidelity without sacrificing the full frame rate (22 kHz), hardware complexity, or optimization time (0.5 s for 1000 frames). Since the dimensionality limitation is intrinsic to spatial light modulation devices and sparse-to-random transformation to complex media, our methods can be generalized to different pattern types, complex media, and device settings, supporting consistent superior performance across different types of complex media with up to an 89% increase in projection accuracy and a 126% improvement in speckle suppression. The proposed optimization framework has the potential to enhance existing holographic setups without any change to the hardware, enable high-fidelity and high-speed wavefront shaping through different scattering media and platforms, and directly facilitate a wide range of physics and real-world applications.

physics.optics↗

arXiv2020

Deep Semantic Matching with Foreground Detection and Cycle-Consistency

Establishing dense semantic correspondences between object instances remains a challenging problem due to background clutter, significant scale and pose differences, and large intra-class variations. In this paper, we address weakly supervised semantic matching based on a deep network where only image pairs without manual keypoint correspondence annotations are provided. To facilitate network training with this weaker form of supervision, we 1) explicitly estimate the foreground regions to suppress the effect of background clutter and 2) develop cycle-consistent losses to enforce the predicted transformations across multiple images to be geometrically plausible and consistent. We train the proposed model using the PF-PASCAL dataset and evaluate the performance on the PF-PASCAL, PF-WILLOW, and TSS datasets. Extensive experimental results show that the proposed approach performs favorably against the state-of-the-art methods.

cs.CV↗