Terahertz (THz) single-pixel imaging has emerged as a significant research direction due to its cost-effectiveness, low system complexity, and high scalability. However, practical implementation is hindered by a critical trade-off between imaging speed and quality, stemming from the combined limitations of modulator response speeds and reconstruction algorithms. Addressing this challenge, we propose a physics-enhanced deep learning framework for video-rate THz single-pixel imaging. By utilizing a high-speed vertical-cavity surface-emitting laser array to pump a high-resistivity silicon wafer, we achieve high-speed THz modulation and remove the refresh-rate limitation associated with commonly used DMD-based optical pattern generation. To bridge the gap between hardware addressing constraints and the requirements for complex encoding masks, a low-rank matrix synthesis strategy is integrated into the neural network training to jointly optimize mask distribution and image reconstruction. Both simulation and experimental results demonstrate that this synergistic mechanism outperforms pure data-driven algorithms and traditional basis scanning methods, enabling real-time THz video capture at 50 f/s with a resolution of 32 × 32 pixels. Even at a 25% sampling ratio, the system achieves high-fidelity reconstruction, effectively balancing speed and quality. This hardware–software co-designed approach paves the way for real-time THz applications in industrial and scientific research while establishing a new paradigm for high-speed dynamic imaging across other spectral regimes.

We report a high-speed terahertz (THz) single-pixel video imaging system integrating comprehensive theoretical modeling and experimental validation. Employing Hadamard and Fourier encoding schemes with a Si3N4/Si wafer modulator driven by a continuous-wave laser, the system achieves a modulation bandwidth of 6 kHz, enabling subwavelength resolution of 1 mm and signal-to-noise ratios exceeding 20. For 32 × 32 pixel images, the Fourier method requires only 200 measurements to attain a peak SNR above 25 dB and a structural similarity index greater than 0.7, enabling video-rate THz imaging. Experimental results demonstrate effective real-time detection of metal targets embedded within paper. Compared with existing methods, the proposed system significantly improves imaging speed, quality, and computational efficiency. This work advances terahertz single-pixel imaging toward practical applications, including remote sensing, nondestructive evaluation, and microscopy. Future research will focus on optimizing sampling and reconstruction to further enhance frame rates and target recognition accuracy.

Terahertz single-pixel imaging technology, which utilizes the unique properties of terahertz waves in combination with a single-pixel detector, offers an effective solution to the scarcity of high-performance focal-plane array detectors in the terahertz regime. However, its widespread application has been hindered primarily by low imaging frame rates. In this study, we propose a high-speed terahertz single-pixel imaging approach based on a vertical-cavity surface-emitting laser array. By integrating a custom-designed 32x32 infrared vertical-cavity surface-emitting laser module with field-programmable gate array-based control and optimized Hadamard encoding, our system overcomes the mechanical switching limitations of conventional digital micromirror devices. Experimental results show the system achieves real-time imaging at 25 frames per second under full sampling. Furthermore, under undersampling conditions, the frame rate reaches 50 fps. Compared to existing methods, our approach improves the imaging speed by an order of magnitude at equivalent resolution while preserving full sampling capability. This advancement offers a fast and efficient solution for dynamic terahertz imaging, with strong potential for applications in security screening, biomedical diagnostics, and beyond.

We propose and demonstrate a single-pixel imaging method based on deep learning network enhanced singular value decomposition. The theoretical framework and the experimental implementation are elaborated and compared with the conventional methods based on Hadamard patterns or deep convolutional autoencoder network. Simulation and experimental results show that the proposed approach is capable of reconstructing images with better quality especially under a low sampling ratio down to 3.12%, or with fewer measurements or shorter acquisition time if the image quality is given. We further demonstrate that it has better anti-noise performance by introducing noises in the SPI systems, and we show that it has better generalizability by applying the systems to targets outside the training dataset. We expect that the developed method will find potential applications based on single-pixel imaging beyond the visible regime.

As an alternative solution to the lack of cost-effective multipixel terahertz cameras, terahertz single-pixel imaging that is free from pixel-by-pixel mechanical scanning has been attracting increasing attention. Such a technique relies on illuminating the object with a series of spatial light patterns and recording with a single-pixel detector for each one of them. This leads to a trade-off between the acquisition time and the image quality, hindering practical applications. Here, we tackle this challenge and demonstrate high-efficiency terahertz single-pixel imaging based on physically enhanced deep learning networks for both pattern generation and image reconstruction. Simulation and experimental results show that this strategy is much more efficient than the classical terahertz single-pixel imaging methods based on Hadamard or Fourier patterns, and can reconstruct high-quality terahertz images with a significantly reduced number of measurements, corresponding to an ultra-low sampling ratio down to 1.56%. The efficiency, robustness and generalization of the developed approach are also experimentally validated using different types of objects and different image resolutions, and clear image reconstruction with a low sampling ratio of 3.12% is demonstrated. The developed method speeds up the terahertz single-pixel imaging while reserving high image quality, and advances its real-time applications in security, industry, and scientific research.

In this article, we demonstrate an efficient terahertz single-pixel imaging system incorporating deep learning networks. Experimental results show that by combining a Hadamard single-pixel imaging system with the deep learning network, the sampling time per pattern can be reduced to 1/20 of the conventional system and the number of Hadamard patterns can be reduced to 10% of the pixels while maintaining high image quality with acceptable signal-to-noise ratio above 20 dB and structural similarity of more than 0.85. We thus expect this article to advance the development of a real-time terahertz single-pixel imaging system and promote its applications.

The Kerker effect has been generalized in nanophotonics and meta-optics, and has recently been of great interest by relating it to various fascinating functionalities such as scattering management and perfect transmission, reflection or absorption. One of the most interesting generalizations is the resonant lattice Kerker effect in periodic nanostructures. However, its active tuning has not been explored yet. Here, we report, for the first time, the active control of the resonant lattice Kerker effect in periodic Ge2Se2Te5 nanodisks. By changing the crystalline fraction, we show that the electric dipole surface lattice resonance (ED-SLR), the magnetic dipole resonance (MDR), and thus the resonant lattice Kerker effect are all red-shifted. We therefore realize the transition from the ED-SLR to the resonant lattice Kerker effect, which enables multilevel tuning of reflection, transmission and absorption with modulation depths above 86%. Taking advantage of the MDR redshifts, we also observe broadband and multilevel tuning of transmissions with modulation depth of 87% over a broadband range of 588 nm. Our work establishes a new path for designing high-performance active nanophotonic devices.

The generalized Kerker effects have attracted increasing interests in recent years due to their abilities to manipulate the far-field properties of metasurfaces. However, the dual-polarized generalized Kerker effect enabling different tailoring of orthogonally-polarized electromagnetic waves has not yet been reported. Herein, we demonstrate polarization-controlled dual resonant lattice Kerker effects in periodic silicon nanodisks. By varying the incident angle, the electric dipole and magnetic dipole surface lattice resonances can spectrally overlap, causing zero reflectance and unitary transmittance, i.e., the resonant lattice Kerker effect. The incident angle for achieving this effect can be tuned differently for s- and p-polarizations over large regions by varying the nanodisk size or the lattice periods. The proposed dual-polarized resonant lattice Kerker effects open up avenues for polarization-controlled manipulation of the phase and wavefront of light with metasurfaces.

Resonant lattice Kerker effect in periodic resonators is one of the most interesting generalizations of the Kerker effect that relates to various fascinating functionalities such as scattering management and Huygens metasurfaces. However, so far this effect has been shown to be sensitive to the incident polarization, restricting its applications. Here, we report, for the first time, polarization-independent resonant lattice Kerker effect in metasurfaces composed of periodic Ge 2 Se 2 Te 5 (GST) disks. For such a metasurface of square lattice, the spectrally overlap of the electric dipole and magnetic dipole surface lattice resonances can be realized by choosing an appropriate GST crystalline fraction regardless of the incident polarization. The operation wavelength and the required GST crystalline fraction can be conveniently tuned over large ranges since these parameters scale linearly with the disk size and the lattice period, greatly facilitating the design. Making use of the obtained resonant lattice Kerker effect, we realize a reconfigurable and polarization-independent lattice Huygens’ metasurface with a dynamic phase modulation of close to 2 π and high transmittance. This work will advance the engineering of the resonant lattice Kerker effect and promote its applications in phase modulation and wavefront control.

Mie surface lattice resonances (SLRs) supported by periodic all-dielectric nanoparticles emerge from the radiative coupling of localized Mie resonances in individual nanoparticles through Rayleigh anomaly diffraction. To date, it remains challenging to achieve narrow bandwidth and active tuning simultaneously. In this work, we report extremely narrow and actively tunable electric dipole SLRs (ED-SLRs) in Ge2Se2Te5 (GST) metasurfaces. Simulation results show that, under oblique incidence with TE polarization, ED-SLRs with extremely narrow linewidth down to 12 nm and high quality factor up to 409 can be excited in the mid-infrared regime. By varying the incidence angle, the ED-SLR can be tuned over an extremely large spectral region covering almost the entire mid-infrared regime. We further numerically show that, by changing the GST crystalline fraction, the ED-SLR can be actively tuned, leading to nonvolatile, reconfigurable, and narrowband filtering, all-optical multilevel modulation, or all-optical switching with high performance. We expect that this work will advance the engineering of Mie SLRs and will find intriguing applications in optical telecommunication, networks, and microsystems.

We propose an ultra-broadband terahertz bandpass filter with dynamically tunable attenuation based on a graphene-metal hybrid metasurface. The metasurface unit cell is composed of two metal stripes enclosed with a graphene rectangular ring. Results show that when the metasurface is normally illuminated by a terahertz wave polarized along the metal stripes, it can act as an ultra-broadband bandpass filter over the spectral range from 1.49 THz to 4.05 THz, corresponding to a fractional bandwidth of 92%. Remarkably, high transmittance above 90% covering the range from 1.98 THz to 3.95 THz can be achieved. By changing the Fermi level of graphene, we find that the attenuation within the passband can be dynamically tuned from 2% to 66%. We expect that the proposed ultra-broadband terahertz bandpass filter with tunable attenuation will find applications in terahertz communication and detection and sensing systems.

太赫兹成像技术具有透视性、安全性以及光谱分辨能力等独特优点,有着广泛的应用前景。由于太赫兹面阵探测器的技术成熟度低、价格昂贵,太赫兹成像技术在较长时间内以单点扫描方案为主,存在系统复杂、成像耗时长等问题。近年来,基于计算成像算法的太赫兹单像素成像技术发展迅速,成为了获取太赫兹图像的重要途径之一。文章综述了太赫兹单像素计算成像技术的基本原理、技术实现手段和应用前景,总结了现存的一些关键问题,并展望了一些今后可能的发展方向。

The recognition of biometrics is an attractive research field in computer science and technology. As a soft biometric, the iris has the advantages of uniqueness, stability and anti -counterfeiting. Recognizing the gender of a person from the iris image is used in identity verification and security. Monitoring and other fields have broad application prospects. Aiming at the shortcomings of traditional machine learning and shallow neural networks in gender classification of iris image and the advantages of convolutional neural networks in image feature extraction, a residual network (ResNet)-based gender classification of iris image model is proposed, which uses ResNet combined with transfer learning is used for pre-training on ImageNet image dataset. The model is used to train an end -to -end s image gender classifier on the dataset, the accuracy rate reaches 94. 6%. Comparing the trained model with other related models on the same dataset, the results show that the test accuracy and recognition efficiency of this model are better than other models.

We demonstrate an automatic recognition strategy for terahertz (THz) pulsed signals of breast invasive ductal carcinoma (IDC) based on a wavelet entropy feature extraction and a machine learning classifier. The wavelet packet transform was implemented into the complexity analysis of the transmission THz signal from a breast tissue sample. A novel index of energy to Shannon entropy ratio (ESER) was proposed to distinguish different tissues. Furthermore, the principal component analysis (PCA) method and machine learning classifier were further adopted and optimized for automatic classification of the THz signal from breast IDC sample. The areas under the receiver operating characteristic curves are all larger than 0.89 for the three adopted classifiers. The best breast IDC recognition performance is with the precision, sensitivity and specificity of 92.85%, 89.66% and 96.67%, respectively. The results demonstrate the effectiveness of the ESER index together with the machine learning classifier for automatically identifying different breast tissues.

In this work, we propose a novel approach to enhance the gain of a terahertz patch antenna by coating an epsilon-near-zero (ENZ) metamaterial superstrate. The ENZ metamaterial is composed of indium antimonide (InSb) and silicon dioxide multilayers, of which the out-of-plane component of the effective permittivity is close to zero. Results show that by coating the ENZ superstrate the peak gain of the antenna is increased from 5.37 dB to 7.79 dB, corresponding to 45% gain enhancement and greatly improved radiation directivity. We find that the ENZ frequency of the multilayer metamaterial equals to that of semiconductor InSb and thus it can be tuned dynamically. We expect the concept of adding a multilayer ENZ metamaterial superstrate to enhance the antenna gain will find potential applications in other types of terahertz antennas and in antennas in other frequency regimes.

We report the design of broadband highly reflective subwavelength high-index-contrast gratings (HCGs) for both TE and TM polarizations in the visible regime. Results show that high reflectivity above 99% covering 544–726 nm or 510–666 nm can be achieved, corresponding to a fractional bandwidth of Δλ/λ0 = 28.7% or 26.5% for the TM or TE polarization, respectively. We reveal that these broad high-reflectivity bands originate from a blend of multiple leaky modes, similar to the counterparts operating in the near-infrared regime. By investigating the effects of the grating height, period, and width, we find that the broadband high reflectivity requires careful optimization. We expect that this work will advance the engineering of broadband HCG reflectors and promote their applications in the visible regime.

We propose a switchable broadband and wide-angular terahertz asymmetric transmission based on a spiral metasurface composed of metal and VO2 hybrid structures. Results show that asymmetric transmission reaching up to 15% can be switched on or off for circularly polarized terahertz waves when the phase of VO2 transits from the insulting state to the conducting state or reversely. Strikingly, we find that relatively high asymmetric transmission above 10% can be maintained over a broad bandwidth of 2.6-4.0 THz and also over a large incident angular range of 0°-45°. We further discover that as the incident angle increases, the dominant chirality of the proposed metasurface with VO2 in the conducting state can shift from intrinsic to extrinsic chirality. We expect this work will advance the engineering of switchable chiral metasurfaces and promote terahertz applications.

We report the design of ultra-broadband, highly reflective all-dielectric reflectors covering the entire visible regime based on two cascaded subwavelength high-index-contrast gratings (HCGs). We find that the spectral distance between the two gratings’ reflective bandwidths, which should be appropriately designed in order to extend the overall bandwidth for high reflectivity, is analogous to the well-known Rayleigh, Abbe and Sparrow criteria for resolution limits. Results illustrated with TM-polarized normal incidence show that high reflectivity above 98.5% covering 400–800 nm can be achieved for two cascaded HCGs with an appropriate spectral distance. The effects of key structural parameters on the bandwidth extension are discussed with physical insights. We expect this work will advance the engineering and applications of HCGs as ultra-thin, ultra-broadband and all-dielectric reflectors.

A new strategy for preparation of Cu2ZnSn(SxSe1-x)(4) (CZTSSe) thin film solar cells by a two-step sulfurization of Cu-Zn-Sn-Se precursors is introduced. The growth evolution of the CZTSSe films has been characterized compositionally and structurally and the growth mechanism has been revealed. A single-phase CZTSSe film without any secondary phase was observed with a solar cell efficiency of 8.55%. A sulfur rich CZTSSe layer was obtained at grain boundaries which is proposed to be beneficial by forming a hole barrier at grain boundary to reduce the recombination. This solar cell is further characterized using photoluminescence (PL) and capacitance-voltage (C-V) and drive-level capacitance profiling (DLCP) technology.

Plasmonic surface lattice resonances (SLRs) supported by metal nanoparticle arrays have a range of appealing characteristics such as extremely narrow linewidths and greatly enhanced near fields, and thus are attractive in diverse applications. Improving the quality factor of SLRs is important for many applications and thus it has been the focus in this field. In this work, we report high quality out-of-plane SLRs supported by two-dimensional metal nanohemisphere arrays embedded in a symmetric dielectric environment. These SLRs, excited under oblique incidence with TM polarization, can have an ultra-narrow resonant linewidth (∼ 0.9 nm) at visible wavelengths around 715 nm. This corresponds to an exceptionally high quality factor of 794, which is ten times that of the widely-adopted nanorods. We attribute this striking performance to the nanohemisphere geometry, which greatly relaxes the stringent requirement on the height of nanoparticles for supporting out-of-plane SLRs, reducing the absorption loss, and in which the out-of-plane oscillations are much stronger than in-plane ones, leading to stronger inter-particle coupling. The tuning of the resonance wavelength and the quality factor can be explained by a qualitative approach based on the detuning between the Rayleigh anomaly and the localized surface plasmon resonance of an isolated nanoparticle. We expect this work will advance the engineering and applications of high quality SLRs.