Single-pixel Imaging Research Articles

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.

Single Pixel Imaging Based on Multiple Prior Deep Unfolding Network

Single Pixel Imaging Based on Multiple Prior Deep Unfolding Network

A Variational Multi-Scale Error Compensation Network for Single-Pixel Imaging

A Variational Multi-Scale Error Compensation Network for Single-Pixel Imaging

SDD-Net: Self-supervised dual-domain dual-path single-pixel imaging

SDD-Net: Self-supervised dual-domain dual-path single-pixel imaging

Fourier single-pixel imaging based on sampling prediction from intermediate frequencies

The imaging quality of Fourier single pixel imaging (FSI) is determined by the number of samples, therefore higher image quality requires more sampling times. Common low-frequency sampling methods compromise high-frequency signals to reduce the number of samples, but this will reduce the resolution of the reconstructed image. The recent adaptive sampling methods can reconstruct high-quality images with low sampling ratio, but the calculation of sampling trajectories is a time-consuming process. To improve imaging efficiency, we propose a fast sampling method based on intermediate frequency prediction for FSI. This method utilizes the continuity and directionality of important frequency distributions in the Fourier spectrum, according to which regions of important high-frequency components can be quickly predicted by firstly identifying important frequency components on the intermediate frequency ring. The simulation and experiment results demonstrated our method is 9 times faster than existing adaptive sampling methods in predicting sampling trajectories while maintaining similar image reconstruction quality for an image of 256 × 256 pixels under 5% sampling ratio. In addition, our method can efficiently sample important spectral components of images that has different frequency distributions, which proves that this method has the potential for wide applications.

Scalable High-Resolution Single-Pixel Imaging via Pattern Reshaping.

Single-pixel imaging (SPI) is an alternative method for obtaining images using a single photodetector, which has numerous advantages over the traditional matrix-based approach. However, most experimental SPI realizations provide relatively low resolution compared to matrix-based imaging systems. Here, we show a simple yet effective experimental method to scale up the resolution of SPI. Our imaging system utilizes patterns based on Hadamard matrices, which, when reshaped to a variable aspect ratio, allow us to improve resolution along one of the axes, while sweeping of patterns improves resolution along the second axis. This work paves the way towards novel imaging systems that retain the advantages of SPI and obtain resolution comparable to matrix-based systems.

Imaging quality enhancement in photon-counting single-pixel imaging via an ADMM-based deep unfolding network in small animal fluorescence imaging.

Photon-counting single-pixel imaging (SPI) can image under low-light conditions with high-sensitivity detection. However, the imaging quality of these systems will degrade due to the undersampling and intrinsic photon-noise in practical applications. Here, we propose a deep unfolding network based on the Bayesian maximum a posterior (MAP) estimation and alternating direction method of multipliers (ADMM) algorithm. The reconstruction framework adopts a learnable denoiser by convolutional neural network (CNN) instead of explicit function with hand-crafted prior. Our method enhances the imaging quality compared to traditional methods and data-driven CNN under different photon-noise levels at a low sampling rate of 8%. Using our method, the sensitivity of photon-counting SPI prototype system for fluorescence imaging can reach 7.4 pmol/ml. In-vivo imaging of a mouse bearing tumor demonstrates an 8-times imaging efficiency improvement.

All-dielectric metasurface for polarization-multiplexed single-pixel imaging

Integration and miniaturization of multi-channel single-pixel imaging systems have become a developing trend. However, it is challenging to meet such development needs solely relying on traditional optical devices. One feasible solution is the utilization of metasurfaces with multiplexing functionality. Here, we propose and validate an all-dielectric, anisotropic metasurface that provides a random mask with polarization multiplexing for single pixel imaging. The design ensures each mask contains 50% target information, allowing adaptive correlated imaging of different targets without needing to redesign the masks. By optimizing the metasurface, we enhance computational efficiency by preventing correlation between different polarization channels and mask patterns. We also adjust the parameters of the compressed sensing algorithm to accommodate various sampling rates, ensuring high-quality image reconstruction. Additionally, the whole system is simulated by the angular spectrum transmission and compressed sensing reconstruction algorithm, providing image reconstruction results for metasurfaces of different sizes, demonstrating the feasibility of the proposed approach. It is noteworthy that the designed metasurface works for single-wavelength operation and could be extended to multispectral imaging by introducing achromatic metasurface technology. The proposed method could miniaturize the optical devices and reduce light loss.

Single-pixel microscopy with optical sectioning.

Imaging with single-pixel detectors offers a valuable alternative to the conventional focal plane array strategy, especially for wavelengths where silicon-based sensor arrays exhibit lower efficiency. However, the absence of optical sectioning remains a challenge in single-pixel microscopy. In this paper, we introduce a single-pixel microscope with optical sectioning capabilities by integrating single-pixel imaging (SPI) techniques with structured illumination microscopy (SIM) methods. A spatial light modulator positioned at the microscope's input port encodes a series of structured light patterns, which the microscope focuses onto a specific plane of the 3D sample. Simultaneously, a highly sensitive bucket detector captures the light reflected by the object. Optical sectioning is achieved through a high-frequency grating positioned at the microscope's output port, which is conjugated with the spatial light modulator. Utilizing SPI reconstruction techniques and SIM algorithms, our computational microscope produces high-quality 2D images without blurred out-of-focus regions. We validate the performance of the single-pixel microscope (SPM) by measuring the axial response function and acquiring images of various 3D samples in reflected bright-field configuration. Furthermore, we demonstrate the suitability of the optical setup for single-pixel fluorescence microscopy with optical sectioning.

Image authentication method based on Fourier zero-frequency replacement and single-pixel self-calibration imaging by diffractive deep neural network.

The diffractive deep neural network is a novel network model that applies the principles of diffraction to neural networks, enabling machine learning tasks to be performed through optical principles. In this paper, a fully optical authentication model is developed using the diffractive deep neural network. The model utilizes terahertz light for propagation and combines it with a self-calibration single-pixel imaging model to construct a comprehensive optical authentication system with faster authentication speed. The proposed system filters the authentication images, establishes an optical connection with the Fourier zero-frequency response of the illumination pattern, and introduces the signal-to-noise ratio as a criterion for batch image authentication. Computer simulations demonstrate the fast speed and strong automation performance of the proposed optical authentication system, suggesting broad prospects for the combined application of diffractive deep neural networks and optical systems.

True image construction in quantum-secured single-pixel imaging under spoofing attack

True image construction in quantum-secured single-pixel imaging under spoofing attack

High‐sensitive and fast MXene/silicon photodetector for single‐pixel X‐ray imaging

AbstractThe demand for high‐performance X‐ray detectors leads to material innovation for efficient photoelectric conversion and carrier transfer. However, current X‐ray detectors are often susceptible to chemical and irradiation instability, complex fabrication processes, hazardous components, and difficult compatibility. Here, we investigate a two‐dimensional (2D) material with a relatively low atomic number, Ti3C2Tx MXenes, and single crystal silicon for X‐ray detection and single‐pixel imaging (SPI). We fabricate a Ti3C2Tx MXene/Si X‐ray detector demonstrating remarkable optoelectronic performance. This detector exhibits a sensitivity of 1.2 × 107 μC Gyair−1 cm−2, a fast response speed with a rise time of 31 μs, and an incredibly low detection limit of 2.85 nGyair s−1. These superior performances are attributed to the unique charge coupling behavior under X‐ray irradiation via intrinsic polaron formation. The device remains stable even after 50 continuous hours of high‐dose X‐ray irradiation. Our device fabrication process is compatible with silicon‐based semiconductor technology. Our work suggests new directions for eco‐friendly X‐ray detectors and low‐radiation imaging system.image

Compressive confocal microscopy imaging at the single-photon level with ultra-low sampling ratios

Laser-scanning confocal microscopy serves as a critical instrument for microscopic research in biology. However, it suffers from low imaging speed and high phototoxicity. Here we build a novel deep compressive confocal microscope, which employs a digital micromirror device as a coding mask for single-pixel imaging and a pinhole for confocal microscopic imaging respectively. Combined with a deep learning reconstruction algorithm, our system is able to achieve high-quality confocal microscopic imaging with low phototoxicity. Our imaging experiments with fluorescent microspheres demonstrate its capability of achieving single-pixel confocal imaging with a sampling ratio of only approximately 0.03% in specific sparse scenarios. Moreover, the deep compressive confocal microscope allows single-pixel imaging at the single-photon level, thus reducing the excitation light power requirement for confocal imaging and suppressing the phototoxicity. We believe that our system has great potential for long-duration and high-speed microscopic imaging of living cells.

Optimal hadamard single-pixel imaging based on fourier spectrum of pattern

A Hadamard single-pixel imaging method is proposed, which rearranges the order of Hadamard patterns by comparing their energy values of selected regions in the Fourier spectrum of the patterns, thereby optimizing the sampling times required when a certain level of image quality needed to be obtained. The relationship between the Fourier spectrum of the reconstructed image and the adopted projection patterns is explored, and we argue that the reconstructed object is actually a weighted superposition of the applied patterns. Simulation and experiment are carried out for the proposed method. The results show that the selection of pattern is crucial to the reconstruction of the object. We believe that this method may be helpful to the optimal design of single-pixel imaging pattern in the future.

High speed single pixel imaging using a microLED-on-CMOS light projector.

Achieving high frame-rate operation in single pixel imaging schemes normally demands significant compromises in the flexibility of the imaging system, requiring either complex optical setups or a hardware-limited pattern mask set. Here, we demonstrate a single pixel imaging capability with pattern frame-rates approaching 400 kfps with a recently developed microLED light projector and an otherwise simple optical setup. The microLED array has individually addressable pixels and can operate significantly faster than digital micromirror devices, allowing flexibility with regards to the pattern masks employed for imaging even at the fastest frame-rates. Using a full set of Hadamard or Noiselet patterns, we demonstrate 128 × 128 pixel images being generated at 7.3 fps. We generate a pattern set specifically for the light projector using deep learning tools and use these patterns to demonstrate single pixel imaging at almost 800 fps.

Prior-free 3D tracking of a fast-moving object at 6667 frames per second with single-pixel detectors.

Real-time tracking and 3D trajectory computation of fast-moving objects is a promising technology, especially in the field of autonomous driving. However, existing image-based tracking methods face significant challenges when it comes to real-time tracking, primarily due to the limitation of storage space and computational resources. Here, we propose a novel approach that enables real-time 3D tracking of a fast-moving object without any prior motion information and at a very low computational cost. To enable 3D coordinate synthesis with a space-efficient optical setup, geometric moment patterns are projected on two non-orthogonal planes with a spatial resolution of 125 μm. Our experiment demonstrates an impressive tracking speed of 6667 frames per second (FPS) with a 20 kHz digital micromirror device (DMD), which is more than 200 times faster than the widely adopted video-based tracking methods. To the best of our knowledge, this is the highest tracking speed record in the field of single-pixel 3D trajectory tracking. This method promotes the development of real-time tracking techniques with single-pixel imaging (SPI).

Compressive sensing imaging with periodic perturbation induced caustic lens masks in a ripple tank

Terahertz imaging presents immense potential across many fields but the affordability of multiple-pixel imaging equipment remains a challenge for many researchers. To address this, the adoption of single-pixel imaging emerges as a lower-cost option, however, the data acquisition process necessary for reconstructing images is time-intensive. Compressive Sensing, which allows for generation of images using a reduced number of measurements than Nyquist's theorem demands, presents a promising solution but long processing times are still issue particularly large-sized images. Our proposed solution to this issue involves using caustic lens effect induced by perturbations in a ripple tank as a sampling mask. The dynamic nature of the ripple tank introduces randomness into the sampling process and this reduces measurement time by exploiting the inherent sparsity of THz band signals. This work employed Convolutional Neural Network to perform target classification based on the distinct signal patterns acquired through the caustic lens mask. The proposed classifier achieved 99.22% accuracy rate in distinguishing targets shaped like Latin letters. The controlled randomness introduced by the caustic lens mask is believed to play a crucial role in achieving this high accuracy by mitigating overfitting, a common challenge in machine learning.

Uniform-sampling foveated Fourier single-pixel imaging

Inspired by the retina of human eye, foveated imaging is an effective way to achieve high imaging quality and high imaging efficiency. However, with an inherent property of nonuniformity, advantages of foveated imaging cannot be played well due to the confliction with many theories having uniform properties. Thus, an innovative concept “uniform-sampling foveated imaging” is proposed to break the barrier of foveated imaging and uniform imaging. In this work, the concept is used as a uniform-sampling foveated Fourier single-pixel imaging (UFFSI) to achieve high imaging quality and high imaging efficiency of single-pixel imaging (SPI). First, by flexibly using the proposed three kinds of foveated pattern structures of foveated SPI, namely, “circular structure” via log-polar transformation, “rectangular structure” via log-rectilinear transformation and “rotating-rectangle structure” via log-rectilinear-rotation transformation, the total data number is reduced radically by the data redundancy reduction only requiring high resolution (HR) on regions of interest (ROIs). Next, by a nonuniform weight distribution processing based on the innovative concept of “uniform-sampling foveated imaging”, nonuniform sampling is transformed into uniform sampling to obtain the same uniform optical power as the uniform HR FSI, thus, sparse spectrum property and fast Fourier transform of FSI are used accurately and directly to obtain high imaging quality with further reduced measurements and fast reconstruction. Experimentally, in a large-scale scene, at an ultralow sampling ratio of 0.78 % referring to uniform HR FSI with 1024 × 768 pixels, by using UFFSI with 89 % reduction of data redundancy, ROI has a significantly better imaging quality with around 3.3times reduction in reconstruction time. We hope this work can provide a breakthrough point for future foveated imaging and real-time SPI in real life.

Van der Waals Schottky Junction Photodetector with Ultrahigh Rectifying Ratio and Switchable Photocurrent Generation.

Metal-semiconductor junctions play an important role in the development of electronic and optoelectronic devices. A Schottky junction photodetector based on two-dimensional (2D) materials is promising for self-powered photodetection with fast response speed and large signal-to-noise ratio. However, it usually suffers from an uncontrolled Schottky barrier due to the Fermi level pinning effect arising from the interface states. In this work, all-2D Schottky junctions with near-ideal Fermi level depinning are realized, attributed to the high-quality interface between 2D semimetals and semiconductors. We further demonstrate asymmetric diodes based on multilayer graphene/MoS2/PtSe2 with a current rectification ratio exceeding 105 and an ideality factor of 1.2. Scanning photocurrent mapping shows that the photocurrent generation mechanism in the heterostructure switches from photovoltaic effect to photogating effect at varying drain biases, indicating both energy conversion and optical sensing are realized in a single device. In the photovoltaic mode, the photodetector is self-powered with a response time smaller than 100 μs under the illumination of a 405 nm laser. In the photogating mode, the photodetector exhibits a high responsivity up to 460 A/W originating from a high photogain. Finally, the photodetector is employed for single-pixel imaging, demonstrating its high-contrast photodetection ability. This work provides insight into the development of high-performance self-powered photodetectors based on 2D Schottky junctions.

Enhancing Compressive Single-Pixel Imaging With Zig-Zag-Ordered Walsh-Hadamard Light Modulation

Enhancing Compressive Single-Pixel Imaging With Zig-Zag-Ordered Walsh-Hadamard Light Modulation