An adaptive, and what we believe to be, novel fractional wavelet transform (NFRWT)-based denoising method is proposed for background-noise suppression and signal-to-noise ratio (SNR) enhancement of phase-sensitive optical time-domain reflectometry (Φ-OTDR) signals in long-range sensing and complex engineering scenarios. A time-domain coupled wavelet basis incorporating fractional-order phase modulation and translation phase compensation is constructed, and an adaptive fractional-order selection strategy based on NFRWT-domain energy concentration maximization is introduced. By adjusting the fractional-order phase modulation to the phase characteristics of distributed acoustic sensing (DAS) signals, useful signal components can be clearly distinguished from broadband background noise in the matched NFRWT domain. Combined with multiscale wavelet decomposition and soft-threshold shrinkage, the proposed method suppresses broadband background noise while preserving waveform fidelity. Compared with representative denoising algorithms, NFRWT shows improved performance in both 20.2 km long-range sensing and real-world external intrusion monitoring scenarios. In real-world external intrusion monitoring scenarios, NFRWT achieves an average SNR of 63.38 dB and an average noise floor of -84.32 dB rad2/Hz.

Stimulating motile specimens via optogenetics requires high-speed light pattern modulation and projection to track target motion with millisecond-scale low latency. Although a millisecond-latency microscopic projection system has been reported, low-latency volumetric projection remains currently unachieved. Given that most biological specimens possess 3D cell distributions, such low-latency volumetric projection is essential. This paper proposes a high-speed volumetric projection method using a low-latency DMD, a high-speed TAG lens, a camera microscope, a vision system, and fiber illumination. The developed prototype system demonstrated a mean photon-to-photon latency of 6.66 ms and projected six volumetric patterns in 1,218 μs total.

Traditional three-dimensional imaging techniques, such as structured light profilometry or confocal microscopy, have limitations in terms of imaging speed, system complexity, or adaptability to weak texture samples. To overcome these limitations, this paper proposes and validates what we feel is a novel coaxial three-dimensional imaging method based on the longitudinal variation of polarization state of the structured light field. The core innovation of this method lies in generating a structured illumination light field with a polarization state that changes in a predetermined pattern along the optical axis through polarization modulation. The inherent axial polarization gradient of this light field serves as the "optical ruler" for depth encoding. Combined with a polarization resolving detector, the system can decode the three-dimensional topography information of the object surface from the acquired two-dimensional polarization images with a single exposure, achieving rapid three-dimensional measurement without axial scanning. Theoretical analysis clarifies the mathematical model of encoding and reconstruction, and an experimental compact system based on polarization grating and polarization camera has been built. Quantitative tests show that the axial measurement accuracy of this method for highly reflective samples can reach the micron level. Compared with traditional schemes, this method has the advantages of a compact coaxial optical path structure, high stability, and fast single-frame imaging speed, providing a believed to be novel and promising three-dimensional imaging tool for dynamic observation in life sciences and industrial online inspection.

This paper proposes a freeform optical design method based on collaborative optimization of point clouds and normals. The method represents the freeform surface as a discrete point cloud with local normal information, and realizes collaborative optimization of point position updating and normal reconstruction under the guidance of system image quality evaluation. This method avoids dependence on a preset analytical form of the target freeform surface, as well as repeated complex ray intersection calculations with the analytical target freeform surface during optimization. To ensure the stability of the optimization process, the sensitivity of local normal reconstruction parameters and sampled ray density is further analyzed. The method is then applied to the design of an off-axis three mirror telescope with a wide field of view of 30∘×5∘, in which the third mirror is selected as the freeform surface to be optimized. After optimization, the system merit function decreases significantly from 0.3923 to 0.002789. The final design achieves an average RMS spot radius of 5.939µm, and the full field MTF remains above 0.6 at 56 lp/mm. The results show that the proposed method can effectively obtain a discrete freeform surface solution that satisfies the imaging quality requirements of the system.

Ghost imaging (GI) and single-pixel imaging (SPI) enable image reconstruction without spatially resolved detectors, offering access to broad spectral ranges and challenging imaging environments. However, their adoption has been fundamentally limited by the slow switching of mask patterns, which constrains achievable frame rates. Here, we demonstrate ultrafast GI enabled by 25 GHz speckle pattern switching, achieving a spatial-temporal information flux exceeding 15.68 gigapixels per second, which is over two orders of magnitude higher than previous training-data-free GI approaches. By incorporating wavelength-division multiplexing (WDM), we further scale the throughput via parallelized illumination. The WDM-based GI approach is capable of reconstructing 28×28-pixel images with a 10 ns exposure time, corresponding to 100 megaframes per second (Mfps), and captures microsecond-scale dynamic events. This approach opens avenues for studying rapid processes, where conventional cameras are limited by detector bandwidth, readout speed, or cost.

A simple two-step method produces quasi-monolithic all-glass atomic vapor cells. First, optical contact bonding of standard-optical-quality glass components under vacuum or buffer-gas atmosphere achieves initial hermetic sealing at room temperature. Second, femtosecond laser spot welding along the bond interface provides long-term mechanical and thermal stability, yielding a 14-fold increase in tensile strength compared to pure optical contact bonding. Demonstrated geometries include cylindrical and cubic cells with sidewall widths as low as 2 mm. Stacking and welding multiple machined glass substrates with different structures enables a wide variety of cell designs. Cesium D1 absorption spectroscopy confirms hermetic sealing and excellent long-term stability over 10 months, with no detectable gas ingress or cesium loss. The low-temperature process avoids elevated temperatures, allowing future integration of sensitive interior coatings (e.g., paraffin or silane films) to enhance spin coherence for quantum sensing applications.

We present the remote measurement of temperature vs length profiles in up to 50 m of clear (swimming pool) water. A pair of blue gallium-nitride laser diodes, with wavelengths separated by 4 nm, was multiplexed in time as the excitation source. Elastic and Raman returns were collected using two detector channels. The ratio of the Raman measurements can be calibrated to retrieve temperature as a function of depth, while the ratio of the elastic measurements can be used to correct for differential attenuation between the wavelengths. The temperature accuracy decreases with range and is typically better than 0.5 °C at 10 m and 1 °C at 50 m with depth resolution of 0.5-4 m, respectively, using 5 ns excitation pulses.

Our paper reports on a layered hyperbolic metamaterial (HMM) composed of alternating Ag/TiO2 films, designed to achieve high ultraviolet (UV) transmittance with large critical-angle guiding. The multilayer exhibits hyperbolic iso-frequency contours (IFCs) in the UV regime, and the flattened curvature enables efficient beam transmission across a broad angular range, thereby overcoming the diffraction limit. Within the 374-400 nm interval, the HMM demonstrates a high transmittance band above 85%, with a peak of 91% at 385 nm, surpassing the performance of indium tin oxide (ITO) in UV. Remarkably, high transmittance is maintained for incidence angles up to 60°. This wide-angle, high-transmission response arises from enhanced structural plasmon-polariton coupling in the hyperbolic regime, where incident light excites plasmons at successive interfaces, leading to interlayer coupling, energy accumulation, and subsequent release. Time-domain field analysis reveals that the release process exhibits an ultrafast decay time of 3.46 fs, underscoring the potential of this platform for fast response and high normal transmittance UV optoelectronic devices.

Addressing challenges in Cassegrain systems, including emission efficiency reduction due to central obstruction by secondary mirror and beam distortion caused by atmospheric turbulence, this study proposes the use of disturbance-resistant cubic phase modulated beams (CPB). Simulations and experiments demonstrate that hollow CPB (HCPB) can enhance emission efficiency to over 90%. Under atmospheric turbulence, HCPBs exhibit superior echo intensity, increasing by up to 27% compared to Gaussian beams in the most favorable conditions. These results indicate that CPBs can effectively optimize echo intensity and transmission efficiency in Cassegrain systems, offering a promising solution for enhancing the performance of laser ranging systems.

Single-pixel imaging is a technique that utilizes spatial light modulation and a single-point detector to reconstruct images through computational methods. However, under ultra-low measurement rates, existing reconstruction methods still face significant challenges, such as the loss of high-frequency details and noise interference. This paper proposes a wavelet-domain dual-path reconstruction method named WDNet. This approach decomposes the image into high-frequency and low-frequency components in the wavelet domain and reconstructs them separately: the high-frequency components are restored through a lightweight network to recover texture details, while the low-frequency components guide a diffusion process to maintain structural stability. The advantage of this design lies in its ability to address the characteristics of different frequency components in a targeted manner. Experiments demonstrate that under extremely low measurement rates, the proposed method achieves clearer and less noisy reconstruction results in both simulated data and real acquisition systems.

Early-stage breast cancer lesions are often hidden within mammary ducts only a few hundred micrometers in diameter, where conventional silica-based fiber endoscopes suffer from severe mechanical mismatch with ultrasoft tissue. Hydrogel multimode optical fibers (HMMFs) offer excellent mechanical compliance and biocompatibility but are typically hindered by high optical loss and poor long-term stability due to dehydration. Here, we present a novel biocompatible HMMF for imaging and intelligent recognition of breast tumors. Micron-scale polyacrylamide (PAM) hydrogel fibers are continuously fabricated via a draw-spinning process. This process promotes the evaporation of free water while preserving tightly bound water within the polymer network, enabling stable optical guidance from the visible to the near-infrared region with an optical loss as low as 1.05 dB/cm at 980 nm. To decode the complex speckle patterns transmitted through the HMMFs, we develop a deep neural network termed BiMamba-UNet. This model achieves high-fidelity image reconstruction with average structural similarity (SSIM) values of 0.913 and 0.786 on MNIST and Fashion-MNIST datasets, respectively. Crucially, benefiting from the robust hydration retention of the material, the imaging system demonstrates negligible performance degradation over 7 days. Furthermore, as a clinical proof-of-concept, a two-stage framework is proposed to enable the accurate reconstruction and recognition of H&E-stained breast tumor sections. This work establishes a robust platform for flexible multimodal fiber imaging and opens new avenues toward in vivo optical biopsy.

Optical Tamm state (OTS) sensors are of considerable interest for biosensing applications, benefiting from strong localized field confinement and the experimental convenience of normal-incidence excitation geometry. Conventional multilayer devices based on forward design usually require exhaustive parameter scanning, making optimization inefficient in high-dimensional spaces. We present an OTS refractive-index sensor utilizing a BaCO3/TiN/analyte/(SiO2/TiO2) composite structure, whose geometric parameters are globally optimized via a particle swarm optimization (PSO) algorithm. Numerical calculations show that the optimized device achieves a sensitivity of 590.4 nm/RIU and a figure of merit (FOM) of 47.3 RIU-1. The results show that the structures improve OTS sensing performance and are promising for inverse design applications.

Fabry-Pérot (FP) cavities have been demonstrated as frequency-domain selection elements for orbital angular momentum (OAM) states of light, with applications in optical communications and photonic quantum information processing. However, a general analytical model for high-dimensional OAM mode sorting in FP cavities has been lacking. Here, we present an analytical model based on spectral folding in FP cavities. By mapping folded resonance positions onto a single free spectral range (FSR) circle, the cavity design problem is reduced to maximizing the minimum circular spacing between target modes. A unified performance criterion then links this spacing and the cavity finesse to practical sorting metrics such as sorting efficiency and extinction ratio, establishing a capacity boundary for arbitrary finite target sets under a given performance threshold. The bound is tight for uniform-step target sets, for which the optimal cavity geometry is derived in closed form; for general nonuniform sets, the design reduces to an exact finite search over a bounded set of rational candidates. To experimentally validate the model, we examine a representative boundary case involving nine consecutive OAM modes in the p = 0 Laguerre-Gaussian (LG) subspace. Experimental measurements confirm the predicted folded resonance ordering, where the next mode returns to the fundamental's spectral neighborhood. For an equal-weight nine-mode superposition state, the sorting achieves an average efficiency of 93.19% and a mean extinction ratio of 11.41 dB across all target modes, closely matching theoretical predictions.

Virtual reality (VR) provides immersive visual experiences, but conventional VR systems still face limitations such as the vergence-accommodation conflict (VAC), bulky eyepiece optics, and limited optical see-through (OST) capability. Compact Pancake optics can reduce eyepiece axial length, while computer-generated holography (CGH) can provide depth-dependent wavefront reconstruction. Here, we present a full-color multi-depth holographic Pancake (Holo-Pancake) display prototype that integrates Pancake optics and a complex-valued self-attention neural network for phase-only hologram generation. The proposed CGRSANet employs complex-valued grouped residual self-attention and exponential-space relative position bias to achieve efficient multi-depth holographic reconstruction. Numerical and optical experiments demonstrate full-color holographic reconstruction at multiple depth planes. The prototype demonstrates preliminary OST functionality by enabling simultaneous observation of virtual holographic content and real-world scenes. As an exploratory optics-aware optimization strategy, the point spread function (PSF) of the Pancake module is incorporated into the training pipeline to account for part of the optical degradation introduced by the Pancake imaging path. The results show improved local detail preservation compared with non-PSF-optimized results. These results provide a feasible experimental step toward combining CGH with Pancake optics for multi-depth near-eye display systems.

We present a self-supervised deep-learning-based ghost imaging (GI) reconstruction method, which provides a significant improvement in reconstruction quality for noisy acquisitions among unsupervised methods. We present the supporting mathematical framework and results from both theoretical and real data (from a recent synchrotron X-ray GI experiment). Self-supervision removes the need for clean reference data while offering strong noise reduction. This provides the necessary tools for addressing signal-to-noise ratio concerns for GI acquisitions in emerging and cutting-edge low-light GI scenarios. Notable examples include micro- and nano-scale x-ray emission imaging, e.g., x-ray fluorescence imaging of dose-sensitive samples. Their applications include in-vivo and in-operando case studies for biological samples and batteries.