arXiv subjects

Shang-Hua Yang

Publications and source records attributed to Shang-Hua Yang.

10 recordsLinked to original sources

arXiv2025

Real-time Terahertz Compressive Optical-Digital Neural Network Imaging

Terahertz (THz) band has recently garnered significant attention due to its exceptional capabilities in non-invasive, non-destructive sensing, and imaging applications. However, current THz imaging systems encounter substantial challenges owing to hardware limitations, which result in information loss and restricted imaging throughput during data digitization and information extraction processes. To overcome these challenges, we propose a hybrid compressive optical-digital neural network designed to facilitate both real-time THz imaging and precise object information extraction. This approach utilizes a physical encoder, an optical neural network (ONN), to transform and reduce the dimensionality of physical signals, effectively compressing them to fit the physical constraints of THz sensor arrays. After the compressed signals are captured and digitized by the THz sensor array, a jointly trained digital neural network (DNN) reconstructs the signals into their desired or original form. Our proposed THz ONN-DNN computational imaging system demonstrates enhanced imaging quality, an expanded field of view with a lens-free system, diffraction-free imaging capability, and real-time THz video capture at a rate of two frames per second.

physics.optics↗

arXiv2024

Breaking the Limitations with Sparse Inputs by Variational Frameworks (BLIss) in Terahertz Super-Resolution 3D Reconstruction

Data acquisition, image processing, and image quality are the long-lasting issues for terahertz (THz) 3D reconstructed imaging. Existing methods are primarily designed for 2D scenarios, given the challenges associated with obtaining super-resolution (SR) data and the absence of an efficient SR 3D reconstruction framework in conventional computed tomography (CT). Here, we demonstrate BLIss, a new approach for THz SR 3D reconstruction with sparse 2D data input. BLIss seamlessly integrates conventional CT techniques and variational framework with the core of the adapted Euler-Elastica-based model. The quantitative 3D image evaluation metrics, including the standard deviation of Gaussian, mean curvatures, and the multi-scale structural similarity index measure (MS-SSIM), validate the superior smoothness and fidelity achieved with our variational framework approach compared with conventional THz CT modal. Beyond its contributions to advancing THz SR 3D reconstruction, BLIss demonstrates potential applicability in other imaging modalities, such as X-ray and MRI. This suggests extensive impacts on the broader field of imaging applications.

physics.optics↗

arXiv2023

Super-Resolution Surface Reconstruction from Few Low-Resolution Slices

In many imaging applications where segmented features (e.g. blood vessels) are further used for other numerical simulations (e.g. finite element analysis), the obtained surfaces do not have fine resolutions suitable for the task. Increasing the resolution of such surfaces becomes crucial. This paper proposes a new variational model for solving this problem, based on an Euler-Elastica-based regulariser. Further, we propose and implement two numerical algorithms for solving the model, a projected gradient descent method and the alternating direction method of multipliers. Numerical experiments using real-life examples (including two from outputs of another variational model) have been illustrated for effectiveness. The advantages of the new model are shown through quantitative comparisons by the standard deviation of Gaussian curvatures and mean curvatures from the viewpoint of discrete geometry.

math.AP↗

arXiv2023

Making the Invisible Visible: Toward High-Quality Terahertz Tomographic Imaging via Physics-Guided Restoration

Terahertz (THz) tomographic imaging has recently attracted significant attention thanks to its non-invasive, non-destructive, non-ionizing, material-classification, and ultra-fast nature for object exploration and inspection. However, its strong water absorption nature and low noise tolerance lead to undesired blurs and distortions of reconstructed THz images. The diffraction-limited THz signals highly constrain the performances of existing restoration methods. To address the problem, we propose a novel multi-view Subspace-Attention-guided Restoration Network (SARNet) that fuses multi-view and multi-spectral features of THz images for effective image restoration and 3D tomographic reconstruction. To this end, SARNet uses multi-scale branches to extract intra-view spatio-spectral amplitude and phase features and fuse them via shared subspace projection and self-attention guidance. We then perform inter-view fusion to further improve the restoration of individual views by leveraging the redundancies between neighboring views. Here, we experimentally construct a THz time-domain spectroscopy (THz-TDS) system covering a broad frequency range from 0.1 THz to 4 THz for building up a temporal/spectral/spatial/ material THz database of hidden 3D objects. Complementary to a quantitative evaluation, we demonstrate the effectiveness of our SARNet model on 3D THz tomographic reconstruction applications.

eess.IV↗

arXiv2022

Terahertz Spatio-Temporal Deep Learning Computed Tomography

Terahertz computed tomography (THz CT) has drawn significant attention because of its unique capability to bring multi-dimensional object information from invisible to visible. However, current physics-model-based THz CT modalities present low data use efficiency on time-resolved THz signals and low model fusion extensibility, limiting their application fields' practical use. In this paper, we propose a supervised THz deep learning computed tomography (THz DL-CT) framework based on time-domain information. THz DL-CT restores superior THz tomographic images of 3D objects by extracting features from spatio-temporal THz signals without any prior material information. Compared with conventional and machine learning based methods, THz DL-CT delivers at least 50.2%, and 52.6% superior in root mean square error (RMSE) and structural similarity index (SSIM), respectively. Additionally, we have experimentally demonstrated that the pretrained THz DL-CT model can generalize to reconstruct multi-material systems with no prerequisite information. THz CT through the DL data fusion approach provides a new pathway for non-invasive functional imaging in object investigation.

eess.IV↗

arXiv2022

Physics-guided Terahertz Computational Imaging

Visualizing information inside objects is an ever-lasting need to bridge the world from physics, chemistry, biology to computation. Among all tomographic techniques, terahertz (THz) computational imaging has demonstrated its unique sensing features to digitalize multi-dimensional object information in a non-destructive, non-ionizing, and non-invasive way. Applying modern signal processing and physics-guided modalities, THz computational imaging systems are now launched in various application fields in industrial inspection, security screening, chemical inspection and non-destructive evaluation. In this article, we overview recent advances in THz computational imaging modalities in the aspects of system configuration, wave propagation and interaction models, physics-guided algorithm for digitalizing interior information of imaged objects. Several image restoration and reconstruction issues based on multi-dimensional THz signals are further discussed, which provides a crosslink between material digitalization, functional property extraction, and multi-dimensional imager utilization from a signal processing perspective.

eess.IV↗

arXiv2022

HYPERION: Hyperspectral Penetrating-type Ellipsoidal Reconstruction for Terahertz Blind Source Separation

Terahertz (THz) technology has been a great candidate for applications, including pharmaceutic analysis, chemical identification, and remote sensing and imaging due to its non-invasive and non-destructive properties. Among those applications, penetrating-type hyperspectral THz signals, which provide crucial material information, normally involve a noisy, complex mixture system. Additionally, the measured THz signals could be ill-conditioned due to the overlap of the material absorption peak in the measured bands. To address those issues, we consider penetrating-type signal mixtures and aim to develop a blind hyperspectral unmixing (HU) method without requiring any information from a prebuilt database. The proposed HYperspectral Penetrating-type Ellipsoidal ReconstructION (HYPERION) algorithm is unsupervised, not relying on collecting extensive data or sophisticated model training. Instead, it is developed based on elegant ellipsoidal geometry under a very mild requirement on data purity, whose excellent efficacy is experimentally demonstrated.

eess.SP↗

arXiv2021

Seeing through a Black Box: Toward High-Quality Terahertz TomographicImaging via Multi-Scale Spatio-Spectral Image Fusion

Terahertz (THz) imaging has recently attracted significant attention thanks to its non-invasive, non-destructive, non-ionizing, material-classification, and ultra-fast nature for object exploration and inspection. However, its strong water absorption nature and low noise tolerance lead to undesired blurs and distortions of reconstructed THz images. The performances of existing restoration methods are highly constrained by the diffraction-limited THz signals. To address the problem, we propose a novel Subspace-and-Attention-guided Restoration Network (SARNet) that fuses multi-spectral features of a THz image for effective restoration. To this end, SARNet uses multi-scale branches to extract spatio-spectral features of amplitude and phase which are then fused via shared subspace projection and attention guidance. Here, we experimentally construct ultra-fast THz time-domain spectroscopy system covering a broad frequency range from 0.1 THz to 4 THz for building up temporal/spectral/spatial/phase/material THz database of hidden 3D objects. Complementary to a quantitative evaluation, we demonstrate the effectiveness of our SARNet model on 3D THz tomographic reconstruction

cs.MM↗

arXiv2017

Globally stable microresonator Turing pattern formation for coherent high-power THz radiation on-chip

In nonlinear microresonators driven by continuous-wave (cw) lasers, Turing patterns have been studied in the formalism of Lugiato-Lefever equation with emphasis on its high coherence and exceptional robustness against perturbations. Destabilization of Turing pattern and transition to spatio-temporal chaos, however, limits the available energy carried in the Turing rolls and prevents further harvest of their high coherence and robustness to noise. Here we report a novel scheme to circumvent such destabilization, by incorporating the effect of local mode hybridizations, and attain globally stable Turing pattern formation in chip-scale nonlinear oscillators, achieving a record high power conversion efficiency of 45% and an elevated peak-to-valley contrast of 100. The stationary Turing pattern is discretely tunable across 430 GHz on a THz carrier, with a fractional frequency sideband non-uniformity measured at 7.3x10^-14. We demonstrate the simultaneous microwave and optical coherence of the Turing rolls at different evolution stages through ultrafast optical correlation techniques. The free running Turing roll coherence, 9 kHz in 200 ms and 160 kHz in 20 minutes, is transferred onto a plasmonic photomixer for one of the highest power THz coherent generation at room-temperature, with 1.1% optical-to-THz power conversion. Its long-term stability can be further improved by more than two orders of magnitude, reaching an Allan deviation of 6x10^-10 at 100 s, with a simple computer-aided slow feedback control. The demonstrated on-chip coherent high-power Turing-THz system is promising to find applications in astrophysics, medical imaging, and wireless communications.

physics.optics↗

arXiv2013

Broadband Terahertz Modulators based on MEMS-Reconfigurable Mesh Filters

Active tuning and switching of electromagnetic properties of materials is of great importance for controlling their interaction with electromagnetic waves. Superconductors are the only natural materials that exhibit diamagnetic switching at their critical temperatures. Here, we demonstrate a new class of meta-surfaces with electrically-induced diamagnetic switching capability at room temperature. Structural configuration of the subwavelength meta-molecules determines their magnetic response to an external electromagnetic radiation. By reconfiguration of the meta-molecule structure, the strength of the induced magnetic field in the opposite direction to the external magnetic field is varied and the diamagnetic state of the meta-surface is altered, consequently. We demonstrate a significant change in the relative permeability and permittivity of a custom-designed meta-surface with diamagnetic switching capability at terahertz frequencies, enabling terahertz intensity modulation with record high modulation depths and modulation bandwidths through a fully integrated device platform at room temperature.

physics.optics↗