Single-pixel Imaging Research Articles

A Single-Pixel High-Precision Imaging Technique Based on a Discrete Zernike Transform for High-Efficiency Image Reconstructions

Single-pixel imaging (SPI) has attracted increasing attention in recent years because of its advantages in imaging systems. However, a low reconstruction quality and a long reconstruction time have hindered the development of SPI. Hence, in this study, we propose a Zernike SPI (ZSPI) technique to reduce the number of illumination patterns and reconstruction time whilst retaining robustness. First, the ZSPI technique was theoretically demonstrated. Phase-shifting Zernike moment projections were used to illuminate the target and an inverse Zernike transform was used to reconstruct the desired image. In order to prove the feasibility, numerical simulations were carried out with different sample ratios (SRs) ranging from 0.1 to 0.3; an acceptable reconstruction appeared at approximately 0.1. This result indicated that ZSPI could obtain satisfactory reconstruction results at low SRs. Further simulation and physical experiments were compared with different reconstruction algorithms, including noniterative, linear iterative, and nonlinear iterative methods under speckle modulation patterns at a sample of 0.1 in terms of different targets. The results revealed that ZSPI had a higher imaging quality and required less imaging time, particularly for low-frequency targets. The method presented in this study has advantages for the high-efficiency imaging of low-frequency targets, which can provide a new solution for the SPI method.

Information security scheme using deep learning-assisted single-pixel imaging and orthogonal coding.

Providing secure and efficient transmission for multiple optical images has been an important issue in the field of information security. Here we present a hybrid image compression, encryption and reconstruction scheme based on deep learning-assisted single-pixel imaging (SPI) and orthogonal coding. In the optical SPI-based encryption, two-dimensional images are encrypted into one-dimensional bucket signals, which will be further compressed by a binarization operation. By overlaying orthogonal coding on the compressed signals, we obtain the ciphertext that allows multiple users to access with the same privileges. The ciphertext can be decoded back to the binarized bucket signals with the help of orthogonal keys. To enhance reconstruction efficiency and quality, a deep learning framework based on DenseNet is employed to retrieve the original optical images. Numerical and experimental results have been presented to verify the feasibility and effectiveness of the proposed scheme.

Single-pixel color imaging based on a high-performance MAPbI3 perovskite photodetector

Metal halide perovskites with excellent optoelectronic properties show great promise in optical imaging. However, the complex fabrication process of high-density perovskite image sensors limits its development. Herein, we report a simple perovskite color imaging system by combining a single high-performance perovskite photodetector (PD) and the advanced Fourier single-pixel imaging algorithm. We introduce an atomic layer deposition-TiO2 layer and a Spiro-OMeTAD layer to optimize the band energy level arrangement of the perovskite PD, suppressing the dark current and improving the photocurrent of the device, respectively. The optimized perovskite PD with flat spectral response covering all visible wavelength exhibits a high responsivity of 238 mA/W at zero bias, a high special detectivity of 1.13 × 1013 Jones, and a linear dynamic range of 132 dB. In order to bypass the fabrication of the high-density perovskite PD array, we set up a single-pixel imaging system based on an optimized high-performance perovskite PD, demonstrating a high-quality color imaging result. This work provides a perspective for perovskite image sensors and injects vitality into the single-pixel imaging technology.

Diffraction-limited hyperspectral mid-infrared single-pixel microscopy

In this contribution, we demonstrate a wide-field hyperspectral mid-infrared (MIR) microscope based on multidimensional single-pixel imaging (SPI). The microscope employs a high brightness MIR supercontinuum source for broadband (1.55 upmu hbox {m}–4.5 upmu hbox {m}) sample illumination. Hyperspectral imaging capability is achieved by a single micro-opto-electro-mechanical digital micromirror device (DMD), which provides both spatial and spectral differentiation. For that purpose the operational spectral bandwidth of the DMD was significantly extended into the MIR spectral region. In the presented design, the DMD fulfills two essential tasks. On the one hand, as standard for the SPI approach, the DMD sequentially masks captured scenes enabling diffraction-limited imaging in the tens of millisecond time-regime. On the other hand, the diffraction at the micromirrors leads to dispersion of the projected field and thus allows for wavelength selection without the application of additional dispersive optical elements, such as gratings or prisms. In the experimental part, first of all, the imaging and spectral capabilities of the hyperspectral microscope are characterized. The spatial and spectral resolution is assessed by means of test targets and linear variable filters, respectively. At a wavelength of 4.15 upmu hbox {m} a spatial resolution of 4.92 upmu hbox {m} is achieved with a native spectral resolution better than 118.1 nm. Further, a post-processing method for drastic enhancement of the spectral resolution is proposed and discussed. The performance of the MIR hyperspectral microsopce is demonstrated for label-free chemical imaging and examination of polymer compounds and red blood cells. The acquisition and reconstruction of Hadamard sampled 64 times 64 images is achieved in 450 ms and 162 ms, respectively. Thus, combined with an unprecedented intrinsic flexibiliy gained by a tunable field of view and adjustable spatial resolution, the demonstrated design drastically improves the sample throughput in MIR chemical and biomedical imaging.

Interaction-free, single-pixel quantum imaging with undetected photons

A typical imaging scenario requires three basic ingredients: (1) a light source that emits light, which in turn interacts and scatters off the object of interest; (2) detection of the light being scattered from the object and (3) a detector with spatial resolution. These indispensable ingredients in typical imaging scenarios may limit their applicability in the imaging of biological or other sensitive specimens due to unavailable photon-starved detection capabilities and inevitable damage induced by interaction. Here, we propose and experimentally realize a quantum imaging protocol that alleviates all three requirements. By embedding a single-photon Michelson interferometer into a nonlinear interferometer based on induced coherence and harnessing single-pixel imaging technique, we demonstrate interaction-free, single-pixel quantum imaging of a structured object with undetected photons. Thereby, we push the capability of quantum imaging to the extreme point in which no interaction is required between object and photons and the detection requirement is greatly reduced. Our work paves the path for applications in characterizing delicate samples with single-pixel imaging at silicon-detectable wavelengths.

Using an acousto-optic modulator as a fast spatial light modulator.

High-speed spatial light modulators (SLM) are crucial components for free-space communication and structured illumination imaging. Current approaches for dynamical spatial mode generation, such as liquid crystal SLMs or digital micromirror devices, are limited to a maximum pattern refresh rate of 10 kHz and have a low damage threshold. We demonstrate that arbitrary spatial profiles in a laser pulse can be generated by mapping the temporal radio-frequency (RF) waveform sent to an acousto-optic modulator (AOM) onto the optical field. We find that the fidelity of the SLM performance can be improved through numerical optimization of the RF waveform to overcome the nonlinear effect of AOM. An AOM can thus be used as a 1-dimensional SLM, a technique we call acousto-optic spatial light modulator (AO-SLM), which has 50 µm pixel pitch, over 1 MHz update rate, and high damage threshold. We simulate the application of AO-SLM to single-pixel imaging, which can reconstruct a 32×32 pixel complex object at a rate of 11.6 kHz with 98% fidelity.

Noise-Disentangled Single-Pixel Imaging Under Photon-Limited Conditions

Single pixel imaging (SPI) is a well-established computational imaging modality that renders as a practical and innovative tool for sensing across almost the entire spectrum range. Given the specific measurement mode (e.g., bucket in junction with differential), SPI arguably allows for promising signal-to-noise ratio that underpins an economical alternative to high-sensitivity pixelated detectors. However, this common interpretation could be totally collapsed due to the inevitable time-varying interference, making the image fidelity fall far below those using classical detectors. In this work, we present a novel learning-based SPI protocol that maintains outstanding robustness to noise under extremely low-light condition that leads to 10 photons per pixel or even less. Specifically, a noise disentanglement paradigm is implemented across the data and image domains in an unsupervised framework. In experiments, two photon-limited scenarios that have been typically found in fluorescence imaging were investigated. The results validated the compelling superiorities of our strategy over other conventional SPI algorithms. This work substantially improves the reliability and validity of SPI with downstream applications in the field of biomedicine and other scenarios that suffer from limited number of photons.

Adaptive Coarse-to-Fine Single Pixel Imaging With Generative Adversarial Network Based Reconstruction

Single Pixel Imaging (SPI) that only uses one light intensity sensor has been researched extensively as an alternative imaging method. It is proven to work at low light conditions and unconventional wavelength bands where the classical pixel array sensors are limited. However, the major issues of SPI remain as the image quality and computational efficiency. Thus, this paper proposes an adaptive coarse-to-fine (C2F) sampling method to replace the typical uniform sampling method to achieve image reconstruction with better quality. This scalable sampling mechanism is adaptive to the target scene as it will progressively sample according to the image complexity and quality indicator. Subsequently, a deep Generative Adversarial Network (GAN) model is also proposed to improve the time efficiency of the multi-scale image reconstruction. The results show that C2F sampling consistently outperforms uniform sampling in terms of image quality (21% in SSIM, 8% in PSNR and 17% in RMSE). Besides, improvement in the efficiency is also achieved by the proposed GAN reconstruction, whereby the total time taken is only 0.025% of the time taken for the conventional L1 reconstruction method (≈ 4000 times faster). In conclusion, the proposed adaptive C2F SPI using GAN reconstruction method can serve as an optimised solution to improve both the image quality and computational efficiency in SPI.

Time-space united coding spread spectrum single photon counting imaging method

In this paper, we demonstrate a new imaging architecture called time-space united coding spread spectrum single photon counting imaging technique by combining the space coding based single-pixel imaging technology and spread spectrum time coding based scanning imaging technology. This method has the advantages of range ambiguity-free and large time-bandwidth product. Under the interference of noise, this method can accurately restore depth images. In this work, the time-space united correlation nonlinear detection model based on single photon detection, forward imaging model and signal-to-noise ratio model is derived, and the depth image is restored by convex optimization inversion algorithm. The theoretical model and simulation experiments show that compared with the traditional single pixel imaging method based on spatial coding, this method improves the quality of scene reconstruction. Using m-sequence as time coding, imaging has higher noise robustness. In addition, compared with the traditional space coding single pixel imaging technology, the imaging mean square error of the proposed method is reduced by 4/5 and the imaging mean squared error is reduced by 9/10 after introducing the second correlated method. The proposed imaging architecture in this paper may provide a new path for non-scanning lidar imaging methods.

Direct Noise-Resistant Edge Detection with Edge-Sensitive Single-Pixel Imaging Modulation

The majority of edge detection methods are applied after the capture of object photos. Thus, edge detection quality suffers when disturbances occur during imaging. This work proposes an effective edge detection technique for single-pixel imaging (SI). A sequence of edge-sensitive single-pixel imaging (ESI) and single-round edge-sensitive single-pixel imaging (SESI) modulation patterns is specially designed to extract the edges of unknown objects directly without the need for any previous images. The modulation patterns are formed by convolving the SI basis patterns with a second-order differential operator. Compared with existing published edge detection methods, experimental results revealed that the proposed SESI increased the signal-to-noise ratio by at least 228%, thereby reducing the edge detection time by at least half. The edge detection performance of the SESI scheme was also demonstrated on moving objects, with SESI detecting clear edges even when the target was in motion. Moreover, unlike traditional methods, ESI and SESI are immune to light interference and can detect clear edges of objects even if the objects are corrupted by severe interference from laser or light-emitting diode light sources, whereas traditional methods exhibit substantial noise contamination. Consequently, ESI and SESI can lay the groundwork for fast and robust edge detection operations without imaging.

A single-pixel and computational ghost spectral imaging system based on achromatic lens

A single-pixel and computational ghost spectral imaging system based on achromatic lens

Distribution Retrieval of Both Depth and Reflectivity in 3-D Objects via Using Modulated Single Pixel Imaging

3-D objects are illustrated in terms of depth and reflectivity, and these parameters are usually detected by different devices. A modulated single pixel imaging (MSI) method is introduced for simultaneous detections of both depth and reflectivity in 3-D targets. Two modulations for spatial resolution and noise immunity are realized by operations of a digital-micro-mirror device (DMD) and emitted laser amplitude. Laser intensities are modulated at a high frequency up to 100 MHz and demodulated for their amplitude and phase shift. Modulated laser is reflected by the imaged object and collected by a single photodetector. The imaging model is termed as a minimization problem and solved by a proposed modified log-barrier method at optimal iteration steps. The proposed method yields faster convergence and fewer steps of about a half of that used in the typical line-searching method. Two mirrors were positioned in parallel to fabricate a 3-D phantom. The distributions of both depth and surface reflectivity of these mirrors are well reconstructed. Both simulated and experiment data verified the efficacy of proposed MSI on 3-D objects.

Motion Analysis and Compensation of Space-borne Single Pixel Imaging

Motion Analysis and Compensation of Space-borne Single Pixel Imaging

Computational Framework for Turbid Water Single-Pixel Imaging by Polynomial Regression and Feature Enhancement

Computational Framework for Turbid Water Single-Pixel Imaging by Polynomial Regression and Feature Enhancement

Vibration measurement with frequency modulation single-pixel imaging

Vibration measurement with frequency modulation single-pixel imaging

Research Progress of Terahertz Single-Pixel Imaging and Its Dynamic Mask Materials

Research Progress of Terahertz Single-Pixel Imaging and Its Dynamic Mask Materials

Fourier Single-Pixel Imaging Based on Adaptive Down-Sampling in Frequency Domain

Fourier Single-Pixel Imaging Based on Adaptive Down-Sampling in Frequency Domain

Features and Properties of Single-Pixel Imaging Using Speckle Patterns Generated by Multi-Core Fiber

Features and Properties of Single-Pixel Imaging Using Speckle Patterns Generated by Multi-Core Fiber

Dynamic occlusion removal in single-pixel imaging system based on self-check

Single-pixel imaging (SPI) system modulates the object with a series of patterns, records the corresponding measurements of a bucket detector and forms an image by the algorithm of compressed sensing. In this process, if other objects enter into the field of view of SPI, the accuracy of measurement will be seriously affected, and the quality of the reconstructed image will decrease. Owing to the randomness of the reflectivity and shape of the occlusion, it is difficult to effectively separate the disturbed part from the bucket detector signal. To solve this problem, we propose a self-check method based on the characteristics of Hadamard matrix, that is, using the measurement values of bucket detector to verify the correctness of signal. Usually when using the Hadamard matrix as the measurement matrix in SPI, it is divided into complementary positive pattern and negative pattern. The measurements of these two patterns are subtracted to form the image (the difference value marked by <inline-formula><tex-math id="M1">\begin{document}$ l $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20221918_M1.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20221918_M1.png"/></alternatives></inline-formula>). Owing to the complementarity of the two patterns, the sum of the corresponding measurements should be a constant (marked by <inline-formula><tex-math id="M2">\begin{document}$ u $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20221918_M2.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20221918_M2.png"/></alternatives></inline-formula>). When dynamic occlusion appears, the value of <inline-formula><tex-math id="M3">\begin{document}$ u $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20221918_M3.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20221918_M3.png"/></alternatives></inline-formula> will fluctuate significantly, so we choose <inline-formula><tex-math id="M4">\begin{document}$ u $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20221918_M4.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20221918_M4.png"/></alternatives></inline-formula> as the standard to judge whether an occlusion appears. In order to reduce the influence of other factors (such as system noise or fluctuation of the illumination) in the imaging process, we further propose a dynamic occlusion removal method based on the statistical histogram of the values of <inline-formula><tex-math id="M5">\begin{document}$ u $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20221918_M5.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20221918_M5.png"/></alternatives></inline-formula>. We first find the position of the maximum value in the histogram, and then expand from this position to both sides of the histogram. We calculate the area of the expanded region, and stop the expansion when this area is greater than the threshold. Then the <inline-formula><tex-math id="M6">\begin{document}$ l $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20221918_M6.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20221918_M6.png"/></alternatives></inline-formula> corresponding to <inline-formula><tex-math id="M7">\begin{document}$ u $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20221918_M7.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20221918_M7.png"/></alternatives></inline-formula> in the expanded region is the measured value without interference. Experiments show that this method can retain the undisturbed signals of the bucket detector and significantly improve the quality of the reconstructed image. This method is simple and effective, and it is also suitable for general imaging scenes. More importantly, it does not need to introduce additional patterns for verification, which effectively promotes the practical process of single pixel imaging technology.

A Single-Pixel Imaging Based Stereo Deflectometry for 3D Reconstruction of Free-Form Transparent Objects With Parasitic Reflections

A Single-Pixel Imaging Based Stereo Deflectometry for 3D Reconstruction of Free-Form Transparent Objects With Parasitic Reflections