Single-pixel Imaging Research Articles

Single-pixel imaging robust to arbitrary translational motion

Single-pixel imaging robust to arbitrary translational motion

Two-step Fourier single-pixel imaging for secure and efficient hidden information transmission

In the rapidly evolving field of optical information security, single-pixel imaging (SPI) has emerged as a promising technique for hidden information transmission. However, traditional SPI methods face significant challenges, including the need for excessive modulation patterns and the vulnerability of encrypted information during transmission. Furthermore, the field lacks efficient methods to reconstruct both plaintext and ciphertext images from the same set of single-pixel measurements. Here, we propose a novel and efficient encryption strategy for Fourier single-pixel imaging (FSPI) that addresses these critical challenges. Our approach integrates two key innovations: a two-step Fourier-total variation conjugate gradient descent (F-TVCGD) method and a dual-key decryption mechanism. The F-TVCGD method significantly reduces the number of modulation patterns required for image reconstruction, enhancing efficiency and minimizing data redundancy. Our dual-key mechanism enables the reconstruction of both plaintext and ciphertext images from a single set of single-pixel measurements using different decryption keys, significantly enhancing security without compromising efficiency. The incorporation of Fourier symmetric patterns improves the convergence robustness of the symmetric gradient descent (SGD) algorithm, leading to superior performance under challenging conditions such as sparse sampling and noise attacks. Numerical simulations and optical experiments validate our method's improvements in both accuracy and security compared to traditional approaches. Our findings demonstrate that the proposed F-TVCGD and SGD strategies effectively address the challenges of excessive modulation patterns and information vulnerability in FSPI.

Translated object identification for efficient ghost imaging

Alignment of a single-pixel quantum ghost imaging setup is complex and requires extreme precision. Due to misalignment, easily created by human error in the alignment process, reconstructed images are often translated off the central imaging axis. This becomes problematic for intelligent object detection and identification in fast imaging cases, as these algorithms are unable to achieve early image identification. Here, we implemented a U-net algorithm to correctly recognize images in the early reconstruction stage regardless of any off-axis translation. The U-net was trained on a uniquely curated blurred, noised, and off-axis translated dataset. We achieved a 5× reduction in imaging speeds by early image identification in four different translation directions.

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.

MWIR image deep denoising reconstruction based on single-pixel imaging

Single-pixel imaging enables cost-effective and highly sensitive mid-wave infrared imaging, with the added convenience facilitated by deep learning algorithms. However, most networks train separate models depending on compression rates, frequently neglecting real-world infrared noise, leading to inconveniences and suboptimal reconstruction performance. To address these issues, we propose to integrate intrinsic and external deep denoising priors for the reconstruction of compressive mid-wave infrared images from single-pixel measurements. The generalized alternating projection is unfolded into a designed network to harness the benefits of robustness and internal priors. Furthermore, to mitigate real-world noise in measurements, a Swin Transformer U-shaped network that embeds noise variance is introduced to learn external denoise prior. A comparative analysis of simulated and real infrared single-pixel imaging in experiments highlights the promising performance of the proposed method.

High-Quality and Enhanced-Resolution Single-Pixel Imaging Based on Spiral Line Array Laser Source

High-Quality and Enhanced-Resolution Single-Pixel Imaging Based on Spiral Line Array Laser Source

Efficient construction and comparison of Hadamard orderings for single-pixel imaging at large frame size

The Hadamard basis is widely used for single-pixel imaging due to its orthogonal and binary properties. To achieve rapid and efficient imaging, subsampling is commonly performed, which relies on the construction of various optimized matrix orderings. However, the complex construction and substantial memory consumption limit applications at large frame sizes. In this paper, a novel method is proposed for constructing Hadamard basis orderings based on the Gray code sequence, bypassing the need to generate huge complete Hadamard basis. A significant efficiency boost is achieved with a 1000-fold reduction in the time required to construct optimized orderings compared to existing methods. Furthermore, we conducted comprehensive comparisons of the performance of different orderings under large frame sizes through both simulations and experimental validations. Our approach not only simplifies the use of optimized orderings but also significantly improves sampling and reconstruction efficiency, especially for frame sizes of 256 × 256 and higher.

Bright compact ultrabroadband source by orthogonal laser-sustained plasma

Laser-sustained plasma (LSP) source featuring high brightness and broadband spectral coverage is found to be powerful in various fields of scientific and industrial applications. However, the fundamental limit of low conversion efficiency constrains the system compactness and widespread applications of such broadband light sources. In this paper, we propose an innovative orthogonal LSP to break through the conversion efficiency limitation. Driven by the elevated conversion efficiency from absorbed laser power to ultraviolet (UV) emission, a compact broadband source (250–1650 nm) with UV spectral radiance exceeding 210 mW/(mm2⋅sr⋅nm)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${mW}/({{mm}}^{2}\\,\\cdot\\, {sr}\\,\\cdot\\, {nm})$$\\end{document} is achieved with >100 W pump laser. With the plot of a two-dimensional refractive index model, we report an important conceptual advance that the orthogonal design eliminates the influence of the negative lensing effect on laser power density. Experimental results unambiguously demonstrate that we achieve a bright compact UV-VIS-NIR source with negligible thermal loss and the highest conversion efficiency to our knowledge. Significant enhancement of 4 dB contrast-to-noise ratio (CNR) in spectral single-pixel imaging has been demonstrated using the proposed ultrabroadband source. By establishing the quantitative link between pumping optics design and plasma absorption, this work presents a compact broadband source that combines superior conversion efficiency and unprecedented brightness, which is essential to high-speed inspection and spectroscopy applications.

High-accuracy 3D reconstruction of step edge with ray aliasing based on projective parallel single-pixel imaging

Optical 3D measurement technology especially fringe projection profilometry (FPP) is widely utilized in industrial production. However, invalid mixed phases are common when measuring step edges in industrial parts which leads to outliers in reconstructed point cloud and ultimately causes reduction of valid point cloud and dimensional measurement errors. This paper first analyzes reasons for failure of step edge measurement in FPP and illustrates feasibility of parallel single-pixel imaging (PSI) applied to eliminate ray aliasing of step edge. Further for a good balance of accuracy and efficiency, we propose an optimized projective parallel single-pixel imaging (pPSI) method combing with edge detection in wrapped phase generated by pPSI patterns and adaptive bi-direction projection strategy to accurately identify direct correspondence matching points of step edge. Experimental results verified that the proposed method can achieve high accuracy of 3D reconstruction on step edge while taking less measurement time.

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.

Hybrid CNN-Mamba network for single-pixel imaging

Recent progress in single-pixel imaging (SPI) has exhibited remarkable performance using deep neural networks, e.g., convolutional neural networks (CNNs) and vision Transformers (ViTs). Nonetheless, it is challenging for existing methods to well model object image from single-pixel detections that have a long-range dependency, where CNNs are constrained by their local receptive fields, and ViTs suffer from high quadratic complexity of attention mechanism. Inspired by the Mamba architecture, known for its proficiency in handling long sequences and global contextual information with enhanced computational efficiency as state space models (SSMs), we propose a hybrid network of CNN and Mamba for SPI, named CMSPI. The proposed CMSPI integrates the local feature extraction capability of convolutional layers with the abilities of SSMs for efficiently capturing the long-range dependency, and the design of complementary split-concat structure, depthwise separable convolution, and residual connection enhance learning power of network model. Besides, CMSPI adopts a two-step training strategy, which makes reconstruction performance better and hardware-friendly. Simulations and real experiments demonstrate that CMSPI has higher imaging quality, lower memory consumption, and less computational burden than the state-of-the-art SPI methods.

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.

Near-field terahertz single-pixel imaging with ultralow sampling ratio

Near-field terahertz single-pixel imaging with ultralow sampling ratio

Adaptive sampling strategy for Fourier single-pixel imaging

Fourier single-pixel imaging is a technique that utilizes Fourier bases as illumination patterns to capture images. While the use of orthogonal bases enhances the imaging quality compared with other methods of single pixel imaging, the slow imaging speed has hindered its broader application. Acquiring only low-frequency Fourier coefficients enables capturing a significant portion of image information at low sampling ratio, thereby enhancing imaging speed. Nevertheless, truncating frequencies results in a loss of image detail and a decrease in picture quality. To address this challenge, this paper introduces an adaptive sampling strategy for Fourier single-pixel imaging, allowing for customized sampling distributions for each specific object. The sampling distribution is dynamically generated based on the acquired Fourier coefficients during the acquisition process, eliminating the need for prior information. Through extensive simulations and experimental analysis, this approach has demonstrated its ability to capture more complex image details at equivalent sampling ratios.

Enhancing single-pixel imaging reconstruction using hybrid transformer network with adaptive feature refinement

Single-pixel imaging (SPI) is a novel imaging technique that applies to acquiring spatial information under low light, high absorption, and backscattering conditions. The existing reconstruction techniques, such as pattern analysis and signal-recovery algorithms, are inefficient due to their iterative behaviors and substantial computational requirements. In this paper, we address these issues by proposing a hybrid convolutional-transformer network for efficient and accurate SPI reconstruction. The proposed model has a universal pre-reconstruction layer that can reconstruct the single-pixel measurements collected using any SPI method. Moreover, we utilize the hierarchical encoder-decoder network in U-Net architectures and employ the proposed CONText AggregatIon NEtwoRk (Container) as the adaptive feature refinement module to adaptively leverage the significance of globally and locally enhanced features in SPI reconstruction. As such, we can improve the conventional SPI methods in terms of reconstruction speed and accuracy. Extensive experiments show that the proposed model achieve a significant performance improvement as compared to traditional SPI methods digitally and experimentally while increasing the reconstruction frame rates by threefold. Moreover, the proposed model also outperforms state-of-the-art deep learning models in performing single-pixel imaging reconstruction.

Optical tracking and size estimation of a moving object via time-division multiplexing ghost imaging

Tracking and imaging a moving target via single-pixel imaging requires completion within a short period. Therefore, a low sampling rate is necessary to prevent imaging failure. This paper proposes a method utilizing time-division multiplexing ghost imaging, employing geometric moment patterns and Fourier fringe patterns to obtain real-time target trajectories and Fourier coefficient slices of the scene. After calculating the target’s displacement relative to the initial moment, we perform motion compensation on the slices to gradually estimate the size of the target. The method has been validated as effective and adaptive through optical experiments, requiring only a small amount of sampling relative to the image resolution. Our approach can achieve real-time tracking and estimate the region occupied by the object in the scene and the centroid position relative to the object. It considers the sparsity of objects in space, offering a promising solution for future low-sampling-rate and high-resolution single-pixel imaging of a moving target.

Adaptive Truncation Threshold Determination for Multimode Fiber Single-Pixel Imaging

Truncated singular value decomposition (TSVD) is a popular recovery algorithm for multimode fiber single-pixel imaging (MMF-SPI), and it uses truncation thresholds to suppress noise influences. However, due to the sensitivity of MMF relative to stochastic disturbances, the threshold requires frequent re-determination as noise levels dynamically fluctuate. In response, we design an adaptive truncation threshold determination (ATTD) method for TSVD-based MMF-SPI in disturbed environments. Simulations and experiments reveal that ATTD approaches the performance of ideal clairvoyant benchmarks, and it corresponds to the best possible image recovery under certain noise levels and surpasses both traditional truncation threshold determination methods with less computation—fixed threshold and Stein’s unbiased risk estimator (SURE)—specifically under high noise levels. Moreover, target insensitivity is demonstrated via numerical simulations, and the robustness of the self-contained parameters is explored. Finally, we also compare and discuss the performance of TSVD-based MMF-SPI, which uses ATTD, and machine learning-based MMF-SPI, which uses diffusion models, to provide a comprehensive understanding of ATTD.

Single-pixel complex-amplitude imaging based on untrained complex-valued convolutional neural network

Single-pixel imaging is advancing rapidly in complex-amplitude imaging. However, reconstructing high-quality images demands significant acquisition and heavy computation, making the entire imaging process time-consuming. Here we propose what we believe to be a novel single-pixel complex-amplitude imaging (SCI) scheme using a complex-valued convolutional neural network for image reconstruction. The proposed sheme does not need to pre-train on any labeled data, and can quickly reconstruct high-quality complex-amplitude images with the randomly initialized network only under the constraints of the physical model. Simulation and experimental results show that the proposed scheme is effective and feasible, and can achieve a good balance between efficiency and quality. We believe that this work provides a new image reconstruction framework for SCI, and paves the way for its practical applications.

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.

Contrast change of the test object image in single-pixel and focal-plane array imaging through a scattering medium

Contrast change of the test object image in single-pixel and focal-plane array imaging through a scattering medium