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We demonstrate high-resolution single-pixel imaging (SPI) in the visible and near-infrared wavelength ranges using an SPI framework that incorporates a novel, dedicated sampling scheme and a reconstruction algorithm optimized for the rapid imaging of highly sparse scenes at the native digital micromirror device (DMD) resolution of 1024 × 768. The reconstruction algorithm consists of two stages. In the first stage, the vector of SPI measurements is multiplied by the generalized inverse of the measurement matrix. In the second stage, we compare two reconstruction approaches: one based on an iterative algorithm and the other on a trained neural network. The neural network outperforms the iterative method when the object resembles the training set, though it lacks the generality of the iterative approach. For images captured at a compression of 0.41 percent, corresponding to a measurement rate of 6.8 Hz with a DMD operating at 22 kHz, the typical reconstruction time on a desktop with a medium-performance GPU is comparable to the image acquisition rate. This allows the proposed SPI method to support high-resolution dynamic SPI in a variety of applications, using a standard SPI architecture with a DMD modulator operating at its native resolution and bandwidth, and enabling the real-time processing of the measured data with no additional delay on a standard desktop PC. [ABSTRACT FROM AUTHOR]

Single‐pixel imaging technology can capture images at wavelengths outside the reach of conventional focal plane array detectors. However, the limited image quality and lengthy computational times for iterative reconstruction still hinder its practical application. Recently, single‐pixel imaging based on deep learning has attracted a lot of attention due to its exceptional reconstruction quality and fast reconstruction speed. In this review, an overview of the current status, and the latest advancements of deep learning technologies in the field of single‐pixel imaging are provided. Initially, the fundamental principles of single‐pixel imaging and deep learning, followed by a discussion of their integration and associated benefits are presented. Subsequently, a comprehensive review is conducted on the advancements of deep learning in various domains of single‐pixel imaging, covering super‐resolution single‐pixel imaging, single‐pixel imaging through scattering media, photon‐level single‐pixel imaging, optical encryption based on single‐pixel imaging, color single‐pixel imaging, and image‐free sensing. Finally, open challenges and potential solutions are discussed. [ABSTRACT FROM AUTHOR]

This study presents a cutting-edge imaging technique for special unmanned vehicles (UAVs) designed to enhance tunnel inspection capabilities. This technique integrates ghost imaging inspired by the human visual system with lateral inhibition and variable resolution to improve environmental perception in challenging conditions, such as poor lighting and dust. By emulating the high-resolution foveal vision of the human eye, this method significantly enhances the efficiency and quality of image reconstruction for fine targets within the region of interest (ROI). This method utilizes non-uniform speckle patterns coupled with lateral inhibition to augment optical nonlinearity, leading to superior image quality and contrast. Lateral inhibition effectively suppresses background noise, thereby improving the imaging efficiency and substantially increasing the signal-to-noise ratio (SNR) in noisy environments. Extensive indoor experiments and field tests in actual tunnel settings validated the performance of this method. Variable-resolution sampling reduced the number of samples required by 50%, enhancing the reconstruction efficiency without compromising image quality. Field tests demonstrated the system's ability to successfully image fine targets, such as cables, under dim and dusty conditions, achieving SNRs from 13.5 dB at 10% sampling to 27.7 dB at full sampling. The results underscore the potential of this technique for enhancing environmental perception in special unmanned vehicles, especially in GPS-denied environments with poor lighting and dust. [ABSTRACT FROM AUTHOR]

Depth measurement and three-dimensional (3D) imaging under complex reflection and transmission conditions are challenging and even impossible for traditional structured light techniques, owing to the precondition of point-to-point triangulation. Despite recent progress in addressing this problem, there is still no efficient and general solution. Herein, a Fourier dual-slice projection with depth-constrained localization is presented to separate and utilize different illumination and reflection components efficiently, which can significantly decrease the number of projection patterns in each sequence from thousands to fifteen. Subsequently, multi-scale parallel single-pixel imaging (MS-PSI) is proposed based on the established and proven position-invariant theorem, which breaks the local regional assumption and enables dynamic 3D reconstruction. Our methodology successfully unveils unseen-before capabilities such as (1) accurate depth measurement under interreflection and subsurface scattering conditions, (2) dynamic measurement of the time-varying high-dynamic-range scene and through thin volumetric scattering media at a rate of 333 frames per second; (3) two-layer 3D imaging of the semitransparent surface and the object hidden behind it. The experimental results confirm that the proposed method paves the way for dynamic 3D reconstruction under complex optical field reflection and transmission conditions, benefiting imaging and sensing applications in advanced manufacturing, autonomous driving, and biomedical imaging.

We propose a single-pixel non-imaging target recognition scheme which that exploits the singular values of target objects. By choosing the first few singular values and the corresponding unitary matrices in the singular value decomposition of all the targets, we form the measurement matrices to be projected onto the target in a single-pixel non-imaging scheme. One can quickly and accurately recognize the target images after directly recording the single-pixel signals. From the simulation and experimental results, we found that the accuracy of target recognition was high when the first three singular values were used. The efficiency of target recognition was improved by randomly rearranging the orders of the row vectors in the measurement matrix. Therefore, our research results offer a novel perspective for recognizing non-imaging targets. [ABSTRACT FROM AUTHOR]

The 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Selenium as one of the most historical semiconductors has been used for photovoltaics and photodetection, thanks to its excellent optoelectronic properties, superior stability, facile and low‐cost fabrication. However, commercialized Se‐based devices are mainly based on its amorphous state, which significantly limits the charge transport and requires relatively large bias voltage. The charge carrier dynamics of high‐performance crystalline Se thin films are barely reported, and related photodiodes have not been realized mainly limited by the large dark current and slow response speed. Here, poly‐crystalline Se thin films via vacuum evaporation, in situ thermal annealing, and engineered the interfaces with various metal electrodes are fabricated, which resulted in enhanced charge transport properties and reduced device leakage. The optimized Se photodiodes exhibited excellent performance metrics, including extremely low dark current density, decent specific detectivity, and superior stability. Furthermore, the optimized Se photodetectors are also introduced for single‐pixel imaging, which demonstrates excellent sensitivity and great potential for real applications. [ABSTRACT FROM AUTHOR]

Single-pixel imaging is a method for obtaining an image without multi-pixel sensors. In this method, an object of interest is illuminated by light that has specifically designed spatio-temporal shape, while a single-pixel detector measures the total amount of reflected or transmitted light. The key prerequisite of this method is the ability to create a given spatio-temporal light shape, which becomes especially challenging at the level of single photons. The current photon shaping procedures cannot be directly applied to single-pixel imaging due to the limited generation rate or the need for post-processing. In this work, we propose a highly efficient method for generating arbitrary single-photon shapes without post-selection. We exploit the heralded single-photon shaping approach, and study the effect of the modal properties of a single-photon detection on the characteristics of the heralded photon. We find that the use of multi-mode detection allows to increase the heralding probability of shaped photons proportionally to the number of detected modes while keeping the shape intact, which makes it highly desirable for single-pixel quantum imaging. [ABSTRACT FROM AUTHOR]

Compressed sampling allows to accurately reconstruct a sparse signal even in case of incomplete signal measurements. In this paper, we apply this method to single-pixel imaging and explore the possibilities of image reconstruction by sampling it with an incomplete set of binary light patterns. Using computer simulation, we optimize the image sampling process and find parameters of light patterns such that single-pixel imaging works best. [ABSTRACT FROM AUTHOR]

Abstract The 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.

Abstract This paper presents an efficient scheme for single-pixel imaging (SPI) utilizing a phase-controlled fiber laser array and an untrained deep neural network. The fiber lasers are arranged in a compact hexagonal structure and coherently combined to generate illuminating light fields. Through the utilization of high-speed electro-optic modulators in each individual fiber laser module, the randomly modulated fiber laser array enables rapid speckle projection onto the object of interest. Furthermore, the untrained deep neural network is incorporated into the image reconstructing process to enhance the quality of the reconstructed images. Through simulations and experiments, we validate the feasibility of the proposed method and successfully achieve high-quality SPI utilizing the coherent fiber laser array at a sampling ratio of 1.6%. Given its potential for high emitting power and rapid modulation, the SPI scheme based on the fiber laser array holds promise for broad applications in remote sensing and other applicable fields. Graphical Abstract

We propose and demonstrate a single-pixel imaging method based on deep learning network enhanced singular value decomposition. The theoretical framework and the experimental implementation are elaborated and compared with the conventional methods based on Hadamard patterns or deep convolutional autoencoder network. Simulation and experimental results show that the proposed approach is capable of reconstructing images with better quality especially under a low sampling ratio down to 3.12%, or with fewer measurements or shorter acquisition time if the image quality is given. We further demonstrate that it has better anti-noise performance by introducing noises in the SPI systems, and we show that it has better generalizability by applying the systems to targets outside the training dataset. We expect that the developed method will find potential applications based on single-pixel imaging beyond the visible regime. [ABSTRACT FROM AUTHOR]

The art of light engineering unveils a world of possibilities through the meticulous manipulation of photonic properties such as intensity, phase, and polarization. Precision control over these properties finds application in a variety of fields spanning communications, light–matter interactions, laser direct writing, and imaging. Terahertz (THz) range, nestled between microwaves and infrared light, stands out for its remarkable ability to propagate with minimal losses in numerous dielectric materials and compounds, making THz imaging a powerful tool for noninvasive control and inspection. In this study, a rational framework for the design and optimal assembly of nonparaxial THz imaging systems is established. The research is centered on lensless photonic systems composed solely of high‐resistivity silicon‐based nonparaxial elements such as the Fresnel zone plate, the Fibonacci lens, the Bessel axicon, and the Airy zone plate, all fabricated using laser ablation technology. Through a comprehensive examination through illumination engineering and scattered light collection from raster‐scanned samples in a single‐pixel detector scheme, the imaging systems are evaluated via diverse metrics including contrast, resolution, depth of field, and focus. These findings chart an exciting course toward the development of compact and user‐friendly THz imaging systems where sensors and optical elements seamlessly integrate into a single chip. [ABSTRACT FROM AUTHOR]

Single-pixel imaging allows to obtain images without the use of photosensors with spatial resolution. In this method, an image is calculated by measuring the image conformity to a given set of light patterns by a single-pixel detector. However, when implementing single-pixel imaging in practice, one has to deal with various imperfections, which lead to the difference between the experiment and the idealized theoretical model. In this work, we analyze the effect of detector noise on the ability to compute an image using a compressed sampling algorithm. By conducting computer simulations of single-pixel imaging, we investigate methods for suppressing the effects of detector noise and find optimum parameters of the measurement process. As a result, we demonstrate the ability to obtain images with a realistic model of the detector noise. [ABSTRACT FROM AUTHOR]

This paper presents an efficient scheme for single-pixel imaging (SPI) utilizing a phase-controlled fiber laser array and an untrained deep neural network. The fiber lasers are arranged in a compact hexagonal structure and coherently combined to generate illuminating light fields. Through the utilization of high-speed electro-optic modulators in each individual fiber laser module, the randomly modulated fiber laser array enables rapid speckle projection onto the object of interest. Furthermore, the untrained deep neural network is incorporated into the image reconstructing process to enhance the quality of the reconstructed images. Through simulations and experiments, we validate the feasibility of the proposed method and successfully achieve high-quality SPI utilizing the coherent fiber laser array at a sampling ratio of 1.6%. Given its potential for high emitting power and rapid modulation, the SPI scheme based on the fiber laser array holds promise for broad applications in remote sensing and other applicable fields. [ABSTRACT FROM AUTHOR]

Holographic techniques are indispensable tools for modern optical engineering. Over the past two decades, research about incoherent digital holography has continued to attract attention. Optical scanning holography (OSH) can obtain incoherent holograms using single-pixel detection and structured illumination with Fresnel zone patterns (FZPs). Particularly by changing the size of a detector, OSH can also obtain holograms under coherently illuminated conditions. Since 1979, OSH has continuously evolved. According to the evolution of semiconductor technology, spatial light modulators (SLMs) come to be useful for various imaging fields. By using SLM techniques for OSH, the practicality of OSH is improved. These SLM-based OSH methods are termed computational OSH (COSH). In this review, the configurations, recording and reconstruction methods, and proposed applications of COSH are reviewed. [ABSTRACT FROM AUTHOR]

Strong scattering media imaging has become a great challenge in optical imaging field. In this paper, we propose an active mode single-pixel imaging (SPI) method based on a least-squares conditional generation adversarial network that achieves the imaging through strong scattering media (above 100 NTU) at a low sampling rate of 3.52%. The generator of the proposed network uses a U-shaped network structure with an attention mechanism and integrates squeeze-and-excitation blocks and residual blocks, which can learn the target information in the scattering environment better. The least-square loss, content loss, and mean structural similarity loss are used as total loss functions for the first time to stabilize the training process and avoid the gradient disappearance. Simulation and physical experimental results show that the method can effectively improve the image reconstruction quality of SPI under strong scattering conditions at low sampling rates. This method promotes the development of SPI technology and has important applications in optical scattering medium imaging. [ABSTRACT FROM AUTHOR]

Single-pixel imaging (SPI) leverages sequential pattern illumination and intensity detection to reconstruct images, facing the challenge of balancing high-resolution output with ultra-low sampling rates for rapid imaging processes. We introduce a network architecture specifically tailored for SPI, which demonstrates improved performance even before integrating with SPI’s physical sampling processes. This integration, particularly focusing on the nuanced effects of sampling rates within the model’s loss function and data preprocessing, enhances image reconstruction quality and adaptability at low sampling rates, down to 1.56%. Our approach achieves a balance between advanced computational methods and the physical principles of SPI, resulting in a peak signal-to-noise ratio of 30.93 dB, a structural similarity index measure of 0.8818, and a perceptual index (PI) of 5.31 at a 6.25% sampling rate, alongside a notable PI of 2.68 at a 1.56% sampling rate in practical tests. By merging sophisticated network design with strategic integration of physical sampling rates, our model provides a refined solution for high-quality, high-resolution SPI at minimal sampling rates, facilitating progress in ultra-fast imaging applications.

Single-pixel imaging (SPI) is a novel computational imaging technique which combines illumination light fields and single-pixel detection values to reconstruct the image. Therefore, the generation method of the illumination light fields affects the imaging efficiency and quality. We propose a spiral line array laser source which can generate random illumination light fields without periodicity in the normalized second-order correlation function g(2). It also has a lower full width at half maxima value (FWHM). In numerical simulations and experiments, the compressed sensing based on total variation algorithm is adopted to reconstruct the image. We demonstrate that the novel array is capable of obtaining images of superior quality and resolution compared to existing array laser sources, including hexagonal and Fermat spiral arrays. Combined with the fiber lasers and electro-optical phase modulators, it is expected to achieve high-speed modulation for light fields and high emitting power. Therefore, this method has significant potential for application in remote target detection and recognition.

We propose a single-pixel non-imaging target recognition scheme which that exploits the singular values of target objects. By choosing the first few singular values and the corresponding unitary matrices in the singular value decomposition of all the targets, we form the measurement matrices to be projected onto the target in a single-pixel non-imaging scheme. One can quickly and accurately recognize the target images after directly recording the single-pixel signals. From the simulation and experimental results, we found that the accuracy of target recognition was high when the first three singular values were used. The efficiency of target recognition was improved by randomly rearranging the orders of the row vectors in the measurement matrix. Therefore, our research results offer a novel perspective for recognizing non-imaging targets.

Optical encryption based on single-pixel imaging (SPI) has made great advances with the introduction of deep learning. However, the use of deep neural networks usually requires a long training time, and the networks need to be retrained once the target scene changes. With this in mind, we propose an SPI encryption scheme based on an attention-inserted physics-driven neural network. Here, an attention module is used to encrypt the single-pixel measurement value sequences of two images, together with a sequence of cryptographic keys, into a one-dimensional ciphertext signal to complete image encryption. Then, the encrypted signal is fed into a physics-driven neural network for high-fidelity decoding (i.e., decryption). This scheme eliminates the need for pre-training the network and gives more freedom to spatial modulation. Both simulation and experimental results have demonstrated the feasibility and eavesdropping resistance of this scheme. Thus, it will lead SPI-based optical encryption closer to intelligent deep encryption. [ABSTRACT FROM AUTHOR]

Lensless imaging allows for designing imaging systems that are free from the constraints of traditional imaging architectures. As a broadly investigated technique, mask-modulated lensless imaging encodes light signals via a mask plate integrated with the image sensor, which is more compacted, with scalability and compressive imaging abilities. Here, we review the latest advancements in mask-modulated lensless imaging, lensless image reconstruction algorithms, related techniques, and future directions and applications. [ABSTRACT FROM AUTHOR]

A physics-driven generative adversarial network (GAN) was utilized to demonstrate a single-pixel hyperspectral imaging (HSI) experiment in the infrared spectrum, eliminating the need for extensive dataset training in most data-driven deep neural networks. Within the GAN framework, the physical process of single-pixel imaging (SPI) was integrated into the generator, and its estimated one-dimensional (1D) bucket signals and the actual 1D bucket signals were employed as constraints in the objective function to update the network's parameters and optimize the generator with the assistance of the discriminator. In comparison to single-pixel infrared HSI methods based on compressive sensing and physics-driven convolution neural networks, our physics-driven GAN-based single-pixel infrared HSI exhibits superior imaging performance. It requires fewer samples and achieves higher image quality. We believe that our physics-driven network will drive practical applications in computational imaging, including various SPI-based techniques. [ABSTRACT FROM AUTHOR]

Noise is inevitable in single-pixel imaging (SPI). Although post-processing algorithms can significantly improve image quality, they introduce additional processing time. To address this issue, we propose an online denoising single-pixel imaging scheme at the sampling stage, which uses the filter to optimize the illumination modulation patterns. The image is retrieved through the second-order correlation between the modulation patterns and the intensities detected by the single-pixel detector. Through simulations and experiments, we analyzed the impact of sampling rate, noise intensity, and filter template on the reconstructed images of both binary and grayscale objects. The results demonstrate that the denoising effect is comparable to the imaging-first followed by post-filtering procedures, but the post-processing time is reduced for the same image quality. This method offers a new way for rapid denoising in SPI, and it should be particularly advantageous in applications where time-saving is of paramount importance, such as in image-free large target classification. [ABSTRACT FROM AUTHOR]

The article presents the simulation results of a single-pixel infrared camera image reconstruction obtained by using a convolutional neural network (CNN). Simulations were carried out for infrared images with a resolution of 80 × 80 pixels, generated by a low-cost, low-resolution thermal imaging camera. The study compares the reconstruction results using the CNN and the ℓ1 reconstruction algorithm. The results obtained using the neural network confirm a better quality of the reconstructed images with the same compression rate expressed by the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). [ABSTRACT FROM AUTHOR]

We propose a single-pixel imaging (SPI) method to achieve a higher-resolution image via the Hadamard transform matrix. Unlike traditional SPI schemes, this new method recovers images by correlating single-pixel signals with synchronized transformed patterns of Hadamard bases that are actually projected onto the digital micromirror device. Each transform pattern is obtained through the inverse Fourier transform of the pattern acquired by Gaussian filtering of each Hadamard basis in the frequency domain. The proposed scheme is based on a typical SPI experimental setup and does not add any hardware complexity, enabling the transformation of Hadamard matrices and image reconstruction through data processing alone. Therefore, this approach could be considered as an alternative option for achieving fast SPI in a diffraction-limited imaging system, without the need for additional hardware. [ABSTRACT FROM AUTHOR]

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.

High numerical apertures can result in distortions appearing in a single-shot image, rendering the acquisition of usable images challenging, if not outright impossible. However, in the realm of single-pixel imaging, various strategies can be employed to effectively inspect objects with an excellent resolution, contrast and brightness. Recent advancements in flat photonic components have facilitated the development of compact nonparaxial imaging systems, which show great promise, particularly in the THz range of wavelengths. These innovations hold the potential to advance fields such as communication, material inspection and spectroscopy. In this study, we delve into the imaging of semi-transparent objects with varying levels of detail. Furthermore, we introduce a nonparaxial design for a flat hyperbolical lens and evaluate its performance in these imaging scenarios, comparing it to structured illumination techniques involving Airy, Bessel, and common thin lens configurations. We present findings regarding potential improvements in imaging attributable to the nonparaxial hyperbolical lens. [ABSTRACT FROM AUTHOR]

The combination of single-pixel imaging and single photon-counting technology enables ultra-high-sensitivity photon-counting imaging. In order to shorten the reconstruction time of single-photon counting, the algorithm of compressed sensing is used to reconstruct the underdetermined image. Compressed sensing theory based on prior constraints provides a solution that can achieve stable and high-quality reconstruction, while the prior information generated by the network may overfit the feature extraction and increase the burden of the system. In this paper, we propose a novel sparse autoencoder network prior for the reconstruction of the single-pixel imaging, and we also propose the idea of multi-channel prior, using the fully connected layer to construct the sparse autoencoder network. Then, take the network training results as prior information and use the numerical gradient descent method to solve underdetermined linear equations. The experimental results indicate that this sparse autoencoder network prior for the single-photon counting compressed images reconstruction has the ability to outperform the traditional one-norm prior, effectively improving the reconstruction quality. [ABSTRACT FROM AUTHOR]

The single-pixel imaging technique can reconstruct high-quality images using only a bucket detector with no spatial resolution, and the image quality is degraded in order to meet the demands of real-time applications. According to some studies of algorithm performance, the network model performs differently in simulated and real-world experiments. We propose an end-to-end neural network capable of reconstructing 2D images from experimentally obtained 1D signals optimally. In order to improve the image quality of real-time single-pixel imaging, we built a feedback module in the hidden layer of the recurrent neural network to implement feature feedback. The feedback module fuses high-level features of undersampled images with low-level features through dense jump connections and multi-scale balanced attention modules to gradually optimize the feature extraction process and reconstruct high-quality images. In addition, we introduce a learning strategy that combines mean loss with frequency domain loss to improve the network's ability to reconstruct complex undersampled images. In this paper, the factors that lead to the degradation of single-pixel imaging are analyzed, and a network degradation model suitable for physical imaging systems is designed. The experiment results indicate that the reconstructed images utilizing the proposed method have better quality metrics and visual effects than the excellent methods in the field of single-pixel imaging. [ABSTRACT FROM AUTHOR]

Single-pixel imaging lidar is a novel technology that leverages single-pixel detectors without spatial resolution and spatial light modulators to capture images by reconstruction. This technique has potential imaging capability in non-visible wavelengths compared with surface array detectors. An avalanche photodiode (APD) is a device in which the internal photoelectric effect and the avalanche multiplication effect are exploited to detect and amplify optical signals. An encapsulated APD detector, with an APD device as the core, is the preferred photodetector for lidar due to its high quantum efficiency in the near-infrared waveband. However, research into APD detectors in China is still in the exploratory period, when most of the work focuses on theoretical analysis and experimental verification. This is a far cry from foreign research levels in key technologies, and the required near-infrared APD detectors with high sensitivity and low noise have to be imported at a high price. In this present study, an encapsulated APD detector was designed in a linear mode by integrating a bare APD tube, a bias power circuit, a temperature control circuit and a signal processing circuit, and the corresponding theoretical analysis, circuit design, circuit simulation and experimental tests were carried out. Then, the APD detector was applied in the single-pixel imaging lidar system. The study showed that the bias power circuit could provide the APD with an operating voltage of DC 1.6 V to 300 V and a ripple voltage of less than 4.2 mV. Not only that, the temperature control circuit quickly changed the operating state of the Thermo Electric Cooler (TEC) to stabilize the ambient temperature of the APD and maintain it at 25 ± 0.3 °C within 5 h. The signal processing circuit was designed with a multi-stage amplification cascade structure, effectively raising the gain of signal amplification. By comparison, the trial also suggested that the encapsulated APD detector and the commercial Licel detector had a good agreement on the scattered signal, such as a repetition rate and pulse width response under the same lidar environment. Therefore, target objects in real atmospheric environments could be imaged by applying the encapsulated APD detector to the near-infrared single-pixel imaging lidar system. [ABSTRACT FROM AUTHOR]

Single-pixel imaging (SPI) has recently drawn considerable attention as a new imaging technique. An SPI system using a multi-core fiber (MCF-SPI system) that we proposed has the potential to make the system very compact. This study is concerned with the features and reconstruction properties of MCF-SPI system. In this system, the reconstruction quality varies widely depending on the number and placement of cores. It is necessary to use reconstruction algorithms suitable for the system, considering the performance limitations of the patterns, to improve the output reconstruction quality. The features and properties of speckle patterns generated by MCFs with different core layouts and algorithms were investigated to improve the reconstruction performance based on numerical simulation. Four existing algorithms were compared under several conditions to evaluate the algorithms that improve reconstruction quality. Compressive sensing based on total variation is the most compatible algorithm for MCF-SPI. It was confirmed that the MCF-SPI system performs well in terms of imaging quality if a suitable core layout and algorithm for the application are set.

In some complex scene, such as military reconnaissance and other fields, the target object may be partially occluded by some opaque obstacles, which increase the difficulty to obtain the complete object image. In traditional optical imaging system, the lenslet array or the reference beam and special algorithms are usually needed to solve this problem, which increase the complexity of the system. As a computational optical imaging technology, the problem of single-pixel imaging (SPI) for an occluded object has not been researched yet as we know. Here we propose an SPI system to recover the occluded object image. Firstly, the images of object and occlusion from different perspectives are obtained by detecting the light field at different positions. Secondly, the limited conditions of the system parameters are derived by using geometric optics theory to ensure complete imaging. Thirdly, the feasibility of the scheme and the correctness of the limited conditions are verified by experiments. The results in this article can promote the application of SPI technology in many complex practices.

Three-dimensional single-pixel imaging (3D SPI) has become an attractive imaging modality for both biomedical research and optical sensing. 3D-SPI techniques generally depend on time-of-flight or stereovision principle to extract depth information from backscattered light. However, existing implementations for these two optical schemes are limited to surface mapping of 3D objects at depth resolutions, at best, at the millimeter level. Here, we report 3D light-field illumination single-pixel microscopy (3D-LFI-SPM) that enables volumetric imaging of microscopic objects with a near-diffraction-limit 3D optical resolution. Aimed at 3D space reconstruction, 3D-LFI-SPM optically samples the 3D Fourier spectrum by combining 3D structured light-field illumination with single-element intensity detection. We build a 3D-LFI-SPM prototype that provides an imaging volume of ~390 × 390 × 3,800 µm³ and achieves 2.7-µm lateral resolution and better than 37-µm axial resolution. Its capability of 3D visualization of label-free optical absorption contrast is demonstrated by imaging single algal cells in vivo. Our approach opens broad perspectives for 3D SPI with potential applications in various fields, such as biomedical functional imaging. [ABSTRACT FROM AUTHOR]

High numerical apertures lead to an appearance of distortions in a single-shot image, which make obtaining images troublesome if not impossible. These obstacles can be overcome in single-pixel imaging, where different strategies lead to inspection of objects with a good resolution, contrast and brightness. Recent advances in flat photonic elements have enabled the creation of compact nonparaxial imaging systems, which are especially promising in the THz range of wavelengths, bringing advances to such fields as communication, material inspection and spectroscopy. In this work, we dive into the problematics of single-pixel imaging: we introduce an object sample, which we use to investigate the resolution, contrast and brightness of the classical two-lens imaging setup. We evaluate the nonparaxial imaging of the sample and report that the conditions for the best contrast and the best brightness are decoupled in nonparaxial single pixel imaging. To overcome this hurdle, we use two integral image quality assessment techniques from computational imaging theory and estimate the quality of the image in a virtual numerical THz imaging scenario. The localized mean square error metric did not cause additional constraints to the quality of the image, whereas the global mean square error has restricted the range of possible imaging setups. Thus, the computational integral image quality assessment techniques back up the main claim of this study that in the single-pixel imaging the resolution is decoupled from the image brightness. [ABSTRACT FROM AUTHOR]

In this paper, we present a correlation-based imaging technique in a single-pixel imaging scheme using Hadamard basis illumination. The Hadamard basis, which has the characteristics of a two-bit value {−1, 1} and sparsity in its transformed domain, has been used in the illumination patterns and successfully utilized for imaging the incoherent target. It gives image reconstruction even in low-light conditions. Such deterministic patterns also help to solve the problem of large numbers of measurements in single-pixel imaging, and hence simplify the experimental implementation. Furthermore, to compare the quality of imaging with Hadamard basis patterns, we also compare imaging with Fourier basis patterns and simulation results of both methods, namely Hadamard and Fourier basis, are presented. [ABSTRACT FROM AUTHOR]

Obtaining depth-selective images requires gating procedures such as spatial, nonlinear, or coherence gating to differentiate light originating from different depths of the volume of interest. Nonlinear gating requires pulsed excitation sources and excitation probes, limiting easy usage. Coherence gating also requires broadband sources and interferometry requiring specialized stable setups. Spatial gating can be used both for fluorescence and reflection geometry and various light sources and thus has the least requirements on hardware, but still requires the use of a pinhole which makes it difficult to use for photography or widefield imaging schemes. Here, we demonstrate that we can utilize a single digital micromirror device (DMD) to simultaneously function as a dynamic illumination modulator and automatically synchronized dynamic pinhole array to obtain depth-sectioned widefield images. Utilizing the multiplexed measurement advantage of single-pixel imaging, we show that the depth and ballistic light gating of the confocal single pixel imaging scheme can be utilized to obtain images through glare and multiple scattering where conventional widefield imaging fails to recover clear images due to saturation or random scattered noise. [ABSTRACT FROM AUTHOR]

Ghost imaging (GI) reveals its exceptional superiority over conventional cameras in a range of challenging scenarios such as weak illumination or special waveband. For high-performance GI, it is vital to obtain a sequence of high-fidelity bucket signals. However, measurements may suffer from distortion or loss in harsh environments. Here we present multiple description coding ghost imaging, which rests on illumination consisting of different coding patterns to address this challenge. Experimental results indicate that the proposed method is capable of producing satisfactory image even when the sequence of bucket signals is incomplete or highly distorted. This method provides an encouraging boost for GI in practical applications.