Single-pixel Detector Research Articles

Fast autofocusing based on single-pixel moment detection

Traditional image processing-based autofocusing techniques require the acquisition, storage, and processing of large amounts of image sequences, constraining focusing speed and cost. Here we propose an autofocusing technique, which directly and exactly acquires the geometric moments of the target object in real time at different locations by means of a proper image modulation and detection by a single-pixel detector. An autofocusing criterion is then formulated using the central moments, and the fast acquisition of the focal point is achieved by searching for the position that minimizes the criterion. Theoretical analysis and experimental validation of the method are performed and the results show that the method can achieve fast and accurate autofocusing. The proposed method requires only three single-pixel detections for each focusing position of the target object to evaluate the focusing criterion without imaging the target object. The method does not require any active object-to-camera distance measurement. Comparing to local differential methods such as contrast or gradient measurement, our method is more stable to noise and requires very little data compared with the traditional image processing methods. It may find a wide range of potential applications and prospects, particularly in low-light imaging and near-infra imaging, where the level of noise is typically high.

Panoramic single-pixel imaging with megapixel resolution based on rotational subdivision.

Single-pixel imaging (SPI) using a single-pixel detector is an unconventional imaging method that has great application prospects in many fields to realize high-performance imaging. In particular, the recently proposed catadioptric panoramic ghost imaging (CPGI) extends the application potential of SPI to high-performance imaging at a wide field of view (FOV) with recent growing demands. However, the resolution of CPGI is limited by the hardware parameters of the digital micromirror device (DMD), which cannot meet ultrahigh-resolution panoramic imaging needs that require detailed information. Therefore, to overcome the resolution limitation of CPGI, we propose a panoramic SPI based on rotational subdivision (RSPSI). The key of RSPSI is to obtain the entire panoramic scene by the rotation-scanning of a rotating mirror tilted 45°, so that one single pattern that only covers one sub-FOV with a small FOV can complete an uninterrupted modulation on the entire panoramic FOV during a once-through pattern projection. Then, based on temporal resolution subdivision, the image sequence of sub-FOVs subdivided from the entire panoramic FOV can be reconstructed with pixel-level or even subpixel-level horizontal shifting adjacently. Experimental results using a proof-of-concept setup show that the panoramic image can be obtained with 10428 × 543 of 5,662,404 pixels, which is more than 9.6 times higher than the resolution limit of the CPGI using the same DMD. To the best of our knowledge, the proposed RSPSI is the first to achieve a megapixel resolution via SPI, which can provide potential applications in fields requiring imaging with ultrahigh-resolution and wide FOV.

Feasibility of a Fiber-Dispersive Raman Spectrometer for Biomarker Detection.

Raman spectroscopy is among the top analytical techniques for ultra-low-dense organic matter, crucial to the search for life and analysis of celestial body surfaces in space exploration missions. Achieving the ultimate sensitivity of in-situ Raman spectroscopy necessitates a breakthrough in detecting inelastically scattered light. Single-photon detectors (SPDs) operating in photon counting mode, which can differentiate between Raman and luminescence responses, are promising candidates for the challenging scientific requirements. Since large SPD arrays are not yet commercially available, a dispersive element can be adapted to a single-pixel detector. By exploiting chromatic dispersion in optical fibers and picosecond-pulsed excitation, we delay the arrivals of different spectral components onto a single-pixel SPD. This method also separates weak Raman signals from stronger luminescence through correlated time-domain measurements. We study the impact of fiber properties and the excitation wavelength of a pulsed laser on the spectral resolution of the fiber-dispersive Raman spectrometer (FDRS). Additionally, we demonstrate the FDRS's potential for studying biomarkers and discuss its feasibility for analyzing inclusions in ice matrices.

Two-layer 3D imaging through semi-transparent surface based on FPP-constrained parallel single-pixel detection

Two-layer 3D imaging through semi-transparent surface based on FPP-constrained parallel single-pixel detection

Ghost imaging Lidar: principle, progress and prospect

Abstract Ghost imaging (GI), as a computational imaging technology, can staringly obtain the target's image by computing the second-order correlation function between the intensity of modulation field and the target's echo signal recorded by a single-pixel detector. Since GI with classical light source was experimentally demonstrated, GI Lidar has been considered to be one of the best application prospects in GI direction and become a research hotspot. With in-depth study and development on GI Lidar in recent years, GI Lidar has achieved significant progress and is expected to be applied in the near future. In this paper, we try to sketch the basic principles and superiorities of two kinds of GI Lidar. Next, with respect to the application of long-range, high-resolution, and high-speed moving target detection and recognition, we introduce the development progress of narrow-pulsed GI Lidar and long-pulsed GI Lidar via heterodyne detection. Finally, the key problems and prospect of GI Lidar are also discussed.

Increasing the Resolution of Transmission Electron Microscopy by Computational Ghost Imaging.

By means of numerical simulations, we demonstrate the innovative use of computational ghost imaging in transmission electron microscopy to retrieve images with a resolution that overcomes the limitations imposed by coherent aberrations. The method requires measuring the intensity on a single pixel detector with a series of structured illuminations. The success of the technique is improved if the probes are made to resemble the sample and the patterns cover the area of interest evenly. By using a simple 8 electrode device as a specific example, a twofold increase in resolution beyond the aberration limit is demonstrated to be possible under realistic experimental conditions.

Encryption-decryption scheme in a single-pixel system based on polarization and Laguerre-Gaussian mode modulation

Optical cryptosystems are crucial for ensuring the security of optical information transmission and storage. The indirect measurement mechanism of single-pixel imaging (SPI) offers a feasible implementation channel for optical cryptosystems. Illumination patterns are encryption keys projected onto the plaintext object, while the intensity collected by the single-pixel detector forms the ciphertext. However, the variations in the object's angular position during SPI measurement generally introduce certain inaccuracies in image reconstruction. And due to SPI's input-output linear mapping relationship, the plaintext is vulnerable to exposure. This proposes an encryption-decryption scheme in a single-pixel system based on polarization and Laguerre-Gaussian (LG) mode modulation. The inherent circular symmetry of LG mode makes the angular position of the object information that can be encrypted, while the intrinsic properties of the object can be represented by polarization. Our system characterizes various polarization parameters of samples serving as reliable plaintext with an error of less than 4.2%, including depolarization, diattenuation, and retardance. For encryption demonstration, LG modes are randomly divided into 5 groups, corresponding to an object at different rotational states. This, combined with 16 polarization modulations, constructs pattern-angle-polarization joint keys, enabling high-security encryption as well as high-fidelity decryption of the mask image, optical axis orientation, and retardance of the test sample. Experimental results demonstrate the effectiveness of our scheme in enhancing the security and information complexity of optical cryptography, offering valuable insights for optical communication and quantum information security.

Single-pixel phase imaging via a complex-valued Zernike basis.

Single-pixel phase imaging (SPPI) utilizes a single-pixel detector combined with interferometry to capture phase information of an unknown field. However, to reconstruct an M × N image, normally M × N modulations and detections are required, leading to a long imaging time for SPPI. Here, a complex-valued Zernike basis SPPI (Zernike-SPPI) is proposed to achieve phase reconstruction at as high quality as possible with a very low sampling ratio. Simulations and experiments demonstrate that Zernike-SPPI achieves better imaging quality at a sampling ratio of less than 10% compared to the state-of-the-art SPPI techniques based on Hadamard basis. This means Zernike-SPPI can obtain a high-quality phase image with less imaging time. This work offers a solution to achieve fast SPPI while guaranteeing quality phase reconstruction as high as possible.

Dual-Wavelength LiDAR with a Single-Pixel Detector Based on the Time-Stretched Method.

In the fields of agriculture and forestry, the Normalized Difference Vegetation Index (NDVI) is a critical indicator for assessing the physiological state of plants. Traditional imaging sensors can only collect two-dimensional vegetation distribution data, while dual-wavelength LiDAR technology offers the capability to capture vertical distribution information, which is essential for forest structure recovery and precision agriculture management. However, existing LiDAR systems face challenges in detecting echoes at two wavelengths, typically relying on multiple detectors or array sensors, leading to high costs, bulky systems, and slow detection rates. This study introduces a time-stretched method to separate two laser wavelengths in the time dimension, enabling a more cost-effective and efficient dual-spectral (600 nm and 800 nm) LiDAR system. Utilizing a supercontinuum laser and a single-pixel detector, the system incorporates specifically designed time-stretched transmission optics, enhancing the efficiency of NDVI data collection. We validated the ranging performance of the system, achieving an accuracy of approximately 3 mm by collecting data with a high sampling rate oscilloscope. Furthermore, by detecting branches, soil, and leaves in various health conditions, we evaluated the system's performance. The dual-wavelength LiDAR can detect variations in NDVI due to differences in chlorophyll concentration and water content. Additionally, we used the radar equation to analyze the actual scene, clarifying the impact of the incidence angle on reflectance and NDVI. Scanning the Red Sumach, we obtained its NDVI distribution, demonstrating its physical characteristics. In conclusion, the proposed dual-wavelength LiDAR based on the time-stretched method has proven effective in agricultural and forestry applications, offering a new technological approach for future precision agriculture and forest management.

Reconstruction algorithm for cross-waveband optical computing imaging

Abstract In a single-pixel fast imaging setup, the data collected by the single-pixel detector needs to be processed by a computer, but the speed of the latter will affect the image reconstruction time. Here we propose two kinds of setups which are able to transform non-visible into visible light imaging, wherein their computing process is replaced by a camera integration mode. The image captured by the camera has a low contrast, so here we present an algorithm that can realize a high quality image in near-infrared to visible cross-waveband imaging. The scheme is verified both by simulation and in actual experiments. The setups demonstrate the great potential for single-pixel imaging and high-speed cross-waveband imaging for future practical applications.

Seeing through fire with one pixel

Seeing through fire is a critical capability in various applications, such as fire rescue, industrial combustion monitoring, and scientific research. However, the intense electromagnetic radiation emitted by flames can saturate and blind conventional imaging systems, making it challenging to visualize objects or scenes obscured by fire. In this paper, we present a novel method for real-time, full-color through-flame imaging using structured illumination and single-pixel detection. By projecting a series of carefully designed light patterns onto the scene and measuring the backscattered light with a single-pixel detector, we can computationally reconstruct the obscured scene while effectively suppressing the flame's contribution to the image. The single-pixel detector's high dynamic range and sensitivity enable it to capture the weak backscattered signal without being overwhelmed by the flame's intense radiation. We demonstrate the method's effectiveness in several experiments, showcasing its ability to image static and dynamic scenes through both steady and turbulent flames at a frame rate of 15 Hz. Furthermore, we show that the proposed method can be extended to full-color imaging by using three single-pixel detectors with different color filters. The results highlight the potential of this approach for enhancing visibility in fire-related scenarios and other challenging imaging conditions. We believe that the integration of this technology into augmented reality systems could provide firefighters and other users with valuable real-time visual information, improving situational awareness and decision-making in critical situations. We also believe the method can be used for seeing through any self-luminous objects.

High Resolution Terahertz (THz) Imaging

Terahertz (THz) imaging is essential for non-contact and non-destructive testing due to its ability to penetrate numerous materials. Typically, the sample is raster-scanned through the beam waist of a confocal optical setup to generate an image in a single-pixel detection scheme. However, the spatial resolution achieved using such imaging configurations remains no less than millimeters, restricting the application of THz imaging. Here in this work, a simple hollow-core metal waveguide (HCMWG) based terahertz imaging setup has been designed and implemented in transmission configuration to record THz hyperspectral images of a sample. The sample is kept in the near-field range of the HCMWG to exploit the THz electric field confinement of the guided mode toward attaining high-resolution imaging. The THz images are acquired by raster scanning the sample in front of the HCMWG output aperture using a single-pixel detection setup. Additionally, spectroscopic sensing using the same setup has been shown by extracting the absorption spectrum of a chemical compound. Further, a plant leaf is used as a sample to demonstrate the applicability of this technique, where the highly resolved transmitted THz image is acquired using the proposed setup. This image is compared with the image recorded using a conventional confocal lens-based THz optical setup. The results show that the technique can resolve subwavelength features (approx. 0.8λ) of the sample under study while preserving spectroscopic information.

Image-free Hu invariant moment measurement by single-pixel detection

The global feature invariant moment plays a crucial role in capturing the feature information of a target. At present, the extraction of the target moment depends on the processing of the two-dimensional image, which leads to the high computational resource occupation due to the large amount of inherent data and the high information redundancy of the image. This reduces the real-time performance of target moment extraction to a certain extent. In order to solve this problem, we introduce a novel strategy for extracting Hu invariant moments, which is based on the combination of single-pixel detection technology and the Hu invariant moments theory. To the best of our knowledge, this is the first proposal of an image-free extraction strategy employing single-pixel detection. The extraction of moments is seamlessly integrated into the single-pixel detection process, facilitating efficient and rapid acquisition, followed by the construction of Hu invariant moments. Theoretical analysis and experimental validation of our proposed method are carried out, demonstrating the effective acquisition of Hu invariant moments. This technology holds substantial application value and prospects in various fields, including target analysis, understanding, classification, and recognition.

Low-sampling and noise-robust single-pixel imaging based on the untrained attention U-Net

The single-pixel imaging (SPI) technique illuminates the object through a series of structured light fields and detects the light intensity with a single-pixel detector (SPD). However, the detection process introduces a considerable amount of unavoidable white noise, which has a detrimental effect on the image quality and limits the applicability of SPI. In this paper, we combine the untrained attention U-Net with the SPI model to reduce noise and achieve high-quality imaging at low sampling rates. The untrained U-Net has the advantage of not requiring pre-training for better generalization. The attention mechanism can highlight the main features of the image, which greatly suppresses the noise and improves the imaging quality. Numerical simulations and experimental results demonstrate that the proposed method can effectively reduce different levels of Gaussian white noise. Furthermore, it can obtain better imaging quality than existing methods at a low sampling rate of less than 10%. This study will expand the application of SPI in complex noise environments.

Spatial-spectral encoding and dictionary optimization in compressive single-pixel hyperspectral imaging based on mutual coherence minimization

A single-pixel detector based hyperspectral system provides an effective way to obtain the spatial-spectral information of target scenes. However, complex spectral dispersion and the substantial number of measurements not only increase the complexity of the system but also decrease the sampling efficiency and the reconstruction accuracy. In this paper, we propose a compressive sensing (CS) theory based single-pixel hyperspectral imaging system. Based on structured illumination, the spatial information is modulated by binary spatial patterns displayed on a liquid crystal on silicon (LCoS), while polarizing elements at specific angles, acting as a serious of filters, modulate the spectral dimension, effectively avoiding spectral dispersion. In terms of sampling efficiency, the application of CS significantly decreases the number of measurements required compared to the Nyquist-Shannon sampling theorem. Besides, to improve the reconstruction accuracy, mutual coherence minimization is employed to optimize the pre-trained dictionary, spatial patterns and filters. Furthermore, a two-step encoding method based on macro-pixel segmentation is proposed to address the issue of low resolution constrained by the size of the dictionary. Compared to the unoptimized system and dictionary, the proposed method achieves more accurate reconstruction results in both spectral and spatial dimensions. This work may provide opportunities for high-resolution single-pixel hyperspectral imaging systems based on CS.

Simultaneous imaging and element differentiation by energy-resolved x-ray absorption ghost imaging.

Based on the x-ray absorption edges of different elements, we simultaneously image and distinguish the composition of three differently shaped components of an object by using energy-resolved x-ray absorption ghost imaging (GI). The initial x-ray beam is spatially modulated by a series of Hadamard matrix masks, and the object is composed of three pieces of Mo, Ag, and Sn foil in the shape of a triangle, square, and circle, respectively. The transmitted x-ray intensity is measured by an energy-resolved single-pixel detector with a spectral resolution better than 0.8 keV. Through correlation of the transmission spectra with the corresponding Hadamard patterns, the spectral image of the sample is reconstructed, with a spatial resolution of 108 µm. Our experiment demonstrates a practical application of spectral ghost imaging, which has important potential for the noninvasive analysis of material composition and distribution in biology, medical science, and many other fields.

Crystal channeling investigations at medical synchrotron regimes

An experimental setup to investigate ion channeling in the hundreds MeV/u energy range is described. A short bent crystal, aligned to the incoming beam by an angular actuator, should deflect ions into a single plane pixel detector used to identify channeled and unchanneled particles. In order to enhance the footprint of channeling on the recorded signals, the incoming particle emittance is tailored on the crystalline plane acceptance by means of massive collimators. Numerical simulations of low energy carbon ions are performed with FLUKA to fully understand the measurement conditions demonstrate the feasibility of the test, to be performed in the experimental room of the National Center for Oncological Hadrontherapy accelerator complex in Pavia, Italy.

Single-pixel compressive imaging via single photon counting

Single-pixel compressive imaging reconstructs a target scene with many pixels by using a single-pixel detector to measure the power variations as small sequences of sampling patterns are applied. While it boasts remarkable capabilities, its practical applications remain a challenge in the photon-starved regime where signal-to-noise is low. To address this challenge, we propose to combine quantum parametric mode sorting (QPMS) and deep neural networks (DNN) to overcome low signal-to-noise for faithful image construction. We benchmark our approach in a telecom-LiDAR system against that using direct photon counting detection. Our results show that with only 25 sampling patterns (corresponding compression ratio ∼0.043%), QPMS plus DNN give structural similarity index measure and peak signal-to-noise ratio on average above 22 dB and 0.9, respectively, much higher than those with direct detection (DD). The details of our targets from QPMS are more clearly compared with from DD. Notably, such high performance is sustained even in the presence of 500 times stronger in-band background noise, while DD fails. The high efficiency and robust noise rejection promise potential applications in various fields, especially in photon-starving scenarios.

High-speed wafer surface defect detection with edge enhancement via optical spatial filtering in serial time-encoded imaging

With the rapid development of the chip and metasurface industries, high-speed defect detection of wafer during semiconductor batch manufacturing has become increasingly important for yield control. Here, we propose a high-speed, macro-scale wafer surface defect detection method with enhanced image edges, via optical spatial filtering in serial time-encoded imaging. By using blazed gratings to spatially project different wavelength components of pulsed light into a one-dimensional line, together with dispersive Fourier time stretching technology, a high-speed serial time-encoded imaging system based on a single pixel detector is constructed for testing the wafer surface defects, and optical spatial filtering is introduced to improve the imaging quality. Experiments show high-pass spatial filtering effectively restrains environmental interference and enhances image edge detection. Small target detection is achieved without a complex image algorithm. Our solution extends optical spatial filtering to serial time-encoded imaging, not only permitting wafer macro scale defect detection with edge enhancement, but also having potential significance for imaging lidar, imaging spectrometers, ghost imaging technology, and more.

Real-time tracing and location for critical parts of moving objects with single-pixel detection

A scenario based on single-pixel input of recognizing and tracing the critical parts of the moving targets has been proposed. The one-dimensional single-pixel matrix is achieved by bucket measurement by making use of pre-training illumination suitable for the target datasets. The designed recognition network can export the real-time position coordinates of the key components of the moving targets through single-pixel detection. Compared with the general random illumination light field, the results of the pre-training illumination show significant improvement in both recognition efficiency and accuracy. The recognition efficiency can reach 100Hz in the experimental detection of moving model planes. It may have important application prospects in military target tracking and hyperspectral remote sensing.