arXiv subjects

Linjun Zhai

Publications and source records attributed to Linjun Zhai.

4 recordsLinked to original sources

arXiv2026

Hessian sparsity-constrained self-supervised network for near-infrared single-photon single-pixel imaging

Near-infrared (NIR) imaging has emerged as an important technology for night vision, remote sensing, and biological imaging, yet conventional array-detector-based systems are often limited by insufficient sensitivity, high cost, and substantial dark noise. Single-pixel imaging (SPI) offers an attractive alternative, enabling single-photon-level NIR imaging by using a cost-effective single-element detector. Nevertheless, SPI remains restricted by photon noise, leading to degraded imaging quality and limited frame rate under extremely low photon flux conditions. Here, we present a Hessian sparsity-constrained self-supervised network (HS3N) for single-photon NIR SPI, which can suppress noise and enable high-fidelity and real-time imaging under ultra-low illumination conditions. The HS3N integrates the physical forward model of SPI with an untrained neural network regularized by both sparsity priors and Hessian-based structural constraints, enabling effective noise suppression while preserving structural fidelity and continuity. Both simulated and experimental results demonstrate that HS3N enables high-fidelity reconstructions under ultra-low NIR photon levels down to ~0.01 photons per pixel. Furthermore, we demonstrate its dynamic capability by monitoring the dynamic evolution and detachment of infrared-absorbing droplets, at a frame rate of ~20 Hz under ~0.19 photons per pixel, highlighting its potential for high-sensitivity infrared inspection. The proposed reconstruction framework paves the way for practical NIR imaging in extreme low light conditions, which can be extended to visible, mid-infrared or terahertz imaging, offering broad potential for photon-efficient sensing across a wide spectral range.

physics.optics↗

arXiv2026

Ultrasensitive infrared-to-visible artificial vision via self-evolving projection guided by single-pixel detection

Infrared detection and visualization are essential for augmenting human perception across diverse fields, ranging from night vision to industrial inspection and bio-imaging. Conventional infrared cameras are often hindered by high cost, bulky architecture, and complex fabrication requirements. Upconversion sensing systems offer a pixel-free and cost-effective alternative solution by upconverting infrared photons into visible-light signals. However, existing upconversion systems suffer from limitations such as high operating voltages, low quantum efficiency, which prevent their applications in photon-starved environments. Here, we report self-evolving infrared-to-visible upconversion with single-pixel detection (SIVIS) that enables real-time upconverted visualization under photon-starved conditions by integrating self-evolving projection with single-pixel sensing. SIVIS iteratively optimizes illumination patterns with a digital micromirror device based on real-time feedback from a single-pixel infrared detector. This self-evolving process enables the autonomous reconstruction of the target's geometric profile. Simultaneously, it projects a co-modulated visible beam onto the object itself or an adjacent screen, rendering the infrared target directly perceptible to the naked eye in real-time. SIVIS achieves sensing and projection without latency under an ultra-low infrared detection limit of 0.11 photons per pixel per frame (sub-pW -cm2 level) benefited from the high sensitivity. Furthermore, we also validate SIVIS to decrypt infrared-encoded anti-counterfeiting features and visualize vascular-like structures embedded within biological tissues. This photon-feedback-driven artificial vision framework offers a scalable and adaptive solution for ultrasensitive infrared vision, opening promising avenues for night vision, biomedical imaging, and sensing under extreme low-light conditions.

physics.optics↗

arXiv2025

Physics-informed neural network enhanced multispectral single-pixel imaging with a chip spectral sensor

Multispectral imaging (MSI) captures data across multiple spectral bands, offering enhanced informational depth compared to standard RGB imaging and benefiting diverse fields such as agriculture, medical diagnostics, and industrial inspection. Conventional MSI systems, however, suffer from high cost, complexity, and limited performance in low-light conditions. Moreover, data-driven MSI methods depend heavily on large, labeled training datasets and struggle with generalization. In this work, we present a portable multispectral single-pixel imaging (MS-SPI) method that integrates a chip-sized multispectral sensor for system miniaturization and leverages an untrained physics-informed neural network (PINN) to reconstruct high-quality spectral images without the need for labeled training data. The physics-informed structure of the network enables the self-corrected reconstruction of multispectral images directly with the input of raw measurements from the multispectral sensor. Our proof-of-concept prototype achieves the reconstruction of 12-channel high-quality spectral images at the sampling rate of 10%. We also experimentally validate its performance under varying sampling rate conditions, by comparing it with conventional compressive sensing algorithms. Furthermore, we demonstrate the application of this technique to an MSI-based image segmentation task, in which spatial regions are discriminated according to their characteristic spectral signatures. This compact, high-fidelity, and portable approach offers promising pathways to lightweight and cost-effective spectral imaging on mobile platforms.

physics.ins-det↗

arXiv2025

Reconfigurable miniaturized computational spectrometer enabled by photoelastic effect

Miniatured computational spectrometers, distinguished by their compact size and lightweight, have shown great promise for on-chip and portable applications in the fields of healthcare, environmental monitoring, food safety, and industrial process monitoring. However, the common miniaturization strategies predominantly rely on advanced micro-nano fabrication and complex material engineering, limiting their scalability and affordability. Here, we present a broadband miniaturized computational spectrometer (ElastoSpec) by leveraging the photoelastic effect for easy-to-prepare and reconfigurable implementations. A single computational photoelastic spectral filter, with only two polarizers and a plastic sheet, is designed to be integrated onto the top of a CMOS sensor for snapshot spectral acquisition. The different spectral modulation units are directly generated from different spatial locations of the filter, due to the photoelastic-induced chromatic polarization effect of the plastic sheet. We experimentally demonstrate that ElastoSpec offers excellent reconstruction accuracy for the measurement of both simple narrowband and complex spectra. It achieves a full width at half maximum (FWHM) error of approximately 0.2 nm for monochromatic inputs, and maintains a mean squared error (MSE) value on the order of 10^-3 with only 10 spectral modulation units. Furthermore, we develop a reconfigurable strategy for enhanced spectra sensing performance through the flexibility in optimizing the modulation effectiveness and the number of spectral modulation units. This work avoids the need for complex micro-nano fabrication and specialized materials for the design of computational spectrometers, thus paving the way for the development of simple, cost-effective, and scalable solutions for on-chip and portable spectral sensing devices.

physics.optics↗