Single-pixel Detector Research Articles

Compressive ghost imaging through scattering media with deep learning.

Imaging through scattering media is challenging since the signal to noise ratio (SNR) of the reflection can be heavily reduced by scatterers. Single-pixel detectors (SPD) with high sensitivities offer compelling advantages for sensing such weak signals. In this paper, we focus on the use of ghost imaging to resolve 2D spatial information using just an SPD. We prototype a polarimetric ghost imaging system that suppresses backscattering from volumetric media and leverages deep learning for fast reconstructions. In this work, we implement ghost imaging by projecting Hadamard patterns that are optimized for imaging through scattering media. We demonstrate good quality reconstructions in highly scattering conditions using a 1.6% sampling rate.

Spectral encoded computational ghost imaging

Multispectral imaging can acquire images of a scene in multiple spectral bands and has been widely applied in many fields, such as satellite remote sensing and geological exploration. Traditional multispectral imaging employs dispersive optical devices or narrowband filters to separate different wavelengths of light and then uses an array detector or many single-pixel detectors to retrieve them separately. These techniques suffer from large data volumes and low efficiency. In this paper, a spectral encoded strategy is proposed to realize multispectral imaging with computational ghost imaging technology. The projected illumination patterns are produced by means of deterministic orthogonal Hadamard basis patterns fused with random spectral encoded matrices, which represent different spectral information. The evolutionary compressive sensing technique is employed to restore the fused multispectral image of the color object of interest, and then a compressive sensing algorithm is applied to restore the spectral channel images. The validity of the technique is verified by a numerical simulation and an experiment. The proposed method allows a multispectral image simultaneously by only one single-pixel detector, which simplifies the structure of the spectral imaging setup and reduces the amount of data acquired and reconstruction time, and thus improving multispectral imaging efficiency.

Real-time terahertz imaging with a single-pixel detector

Terahertz (THz) radiation is poised to have an essential role in many imaging applications, from industrial inspections to medical diagnosis. However, commercialization is prevented by impractical and expensive THz instrumentation. Single-pixel cameras have emerged as alternatives to multi-pixel cameras due to reduced costs and superior durability. Here, by optimizing the modulation geometry and post-processing algorithms, we demonstrate the acquisition of a THz-video (32 × 32 pixels at 6 frames-per-second), shown in real-time, using a single-pixel fiber-coupled photoconductive THz detector. A laser diode with a digital micromirror device shining visible light onto silicon acts as the spatial THz modulator. We mathematically account for the temporal response of the system, reduce noise with a lock-in free carrier-wave modulation and realize quick, noise-robust image undersampling. Since our modifications do not impose intricate manufacturing, require long post-processing, nor sacrifice the time-resolving capabilities of THz-spectrometers, their greatest asset, this work has the potential to serve as a foundation for all future single-pixel THz imaging systems.

Accelerating Monte Carlo modeling of structured-light-based diffuse optical imaging via "photon sharing".

The increasing use of spatially modulated imaging and single-pixel detection techniques demands computationally efficient methods for light transport modeling. Herein, we report an easy-to-implement yet significantly more efficient Monte Carlo (MC) method for simultaneously simulating spatially modulated illumination and detection patterns accurately in 3D complex domains. We have implemented this accelerated algorithm, named "photon sharing," in our open-source MC simulators, reporting 13.6× and 5.5× speedups in mesh- and voxel-based MC benchmarks, respectively. In addition, the proposed algorithm is readily used to accelerate the solving of inverse problems in spatially modulated imaging systems by building Jacobians of all illumination-detection pattern pairs concurrently, resulting in a 12.4-fold speed improvement.

Hadamard transform-based hyperspectral imaging using a single-pixel detector.

In this paper, a single-pixel hyperspectral imager is developed based on the Hadamard transformation. The imager's design, fabrication, signal processing method, and experimental results are discussed. The single-pixel hyperspectral imager works in pushbroom mode and employs both spatial encoding and spectral encoding to acquire the hyperspectral data cube. Hadamard encoding patterns, which are known for their multiplexing advantage to achieve high signal-to-noise ratio (SNR), are used in both encoding schemes. A digital micromirror device (DMD) from Texas Instruments (TI) is used for slow spatial encoding and a resonant scanning mirror in combination with a fixed Hadamard mask is used for fast spectral encoding. In addition, the pushbroom operation can be achieved internally by spatially shifting the location of the Hadamard encoded slit on the DMD, thus the imager is able to acquire 3D data cubes without the need to scan it across the object. Although our experimental results demonstrate the hyperspectral data cubes of various objects over a 450 nm ∼ 750 nm visible spectral range, the proposed imager can be easily configured to be used at other wavelengths due to the single-pixel detection mechanism used.

High-resolution sub-sampling incoherent x-ray imaging with a single-pixel detector

X-ray “ghost” imaging has drawn great attention for its potential to obtain images with a high resolution and lower radiation dose in medical diagnosis, even with only a single-pixel detector. However, it is hard to realize with a portable x-ray source due to its low flux. Here, we demonstrate a computational x-ray ghost imaging scheme where a real bucket detector and specially designed high-efficiency modulation masks are used, together with a robust deep learning algorithm in which a compressed set of Hadamard matrices is incorporated into a multi-level wavelet convolutional neural network. With a portable incoherent x-ray source of ∼37 µm diameter, we have obtained an image of a real object from only 18.75% of the Nyquist sampling rate. A high imaging resolution of ∼10 µm has been achieved, which is required for cancer detection and so represents a concrete step toward the realization of a practical low cost x-ray ghost imaging camera for applications in biomedicine, archeology, material science, and so forth.

Non-Scanning Three-Dimensional Imaging System with a Single-Pixel Detector: Simulation and Experimental Study

Existing scanning laser three-dimensional (3D) imaging technology has slow measurement speed. In addition, the measurement accuracy of non-scanning laser 3D imaging technology based on area array detectors is limited by the resolution and response frequency of area array detectors. As a result, applications of laser 3D imaging technology are limited. This paper completed simulations and experiments of a non-scanning 3D imaging system with a single-pixel detector. The single-pixel detector can be used to achieve 3D imaging of a target by compressed sensing to overcome the shortcomings of the existing laser 3D imaging technology. First, the effects of different sampling rates, sparse transform bases, measurement matrices, and reconstruction algorithms on the measurement results were compared through simulation experiments. Second, a non-scanning 3D imaging experimental platform was designed and constructed. Finally, an experiment was performed to compare the effects of different sampling rates and reconstruction algorithms on the reconstruction effect of 3D imaging to obtain a 3D image with a resolution of 8 × 8. The simulation results show that the reconstruction effect of the Hadamard measurement matrix and the minimum total variation reconstruction algorithm performed well.

Image-free classification of fast-moving objects using "learned" structured illumination and single-pixel detection.

Object classification generally relies on image acquisition and subsequent analysis. Real-time classification of fast-moving objects is a challenging task. Here we propose an approach for real-time classification of fast-moving objects without image acquisition. The key to the approach is to use structured illumination and single-pixel detection to acquire the object features directly. A convolutional neural network (CNN) is trained to learn the object features. The "learned" object features are then used as structured patterns for structured illumination. Object classification can be achieved by picking up the resulting light signals by a single-pixel detector and feeding the single-pixel measurements to the trained CNN. In our experiments, we show that accurate and real-time classification of fast-moving objects can be achieved. Potential applications of the proposed approach include rapid classification of flowing cells, assembly-line inspection, and aircraft classification in defense applications. Benefiting from the use of a single-pixel detector, the approach might be applicable for hidden moving object classification.

Multiple-image authentication based on the single-pixel correlated imaging and multiple-level wavelet transform

Single-pixel correlated imaging has been developed as one of the most promising techniques in the past decades. In this paper, a new mechanism of single-pixel correlated imaging is designed for multiple-image authentication. The object wave is sequentially modulated by a series of phase-only mask pairs embedded into two cascaded spatial light modulators. The modulated wave passes through the input image located in the object plane, and its intensity is recorded as one of the ciphertext values by using a single-pixel bucket detector without spatial resolution. The sparse data of plaintext images are obtained with the multiple-level wavelet transform and used to construct an interim image with the help of binary amplitude masks. The interim image with a smaller size than that of plaintext images is considered as the input of single-pixel correlated imaging, which can guarantee the number of measurements reduced. Simultaneously, the phase-only mask pairs are retrieved from the 2D patterns formed with the row vectors of the spatially orthogonal Hadamard matrix by using an efficient iterative phase retrieval algorithm. Because the redundancy between speckle patterns generated with these phase-only mask pairs is greatly restrained, the number of measurements can be further reduced. Moreover, the related binary amplitude masks are employed as the secret keys so that the security level is also extensively enhanced. Given the above, the proposed method can significantly achieve the goal of multiple-image verification and provide a different research approach for optical authentication based on single-pixel correlated imaging.

Complex field measurement in a single pixel hybrid correlation holography

We propose a new scheme for the recovery of complex-valued objects in a single-pixel hybrid correlation holography. The idea is to generate an intensity correlation hologram from the correlation of intensity fluctuations obtained over two channels, namely an optical channel equipped with a single pixel detector and a digital channel. The scheme has a theoretical basis which is described to reconstruct the objects from a single pixel detector. An experimental arrangement is proposed and as a first step towards realizing/implementing the technique, simulation of the experimental model was carried to image three complex objects.

Energy-Selective X-Ray Ghost Imaging * *Supported by the Science Challenge Project (No. TZ2018005), the Civil Space Project (No. D040301-1), the National Natural Science Foundation of China (Nos. 11991073, 11721404, and 61975229), the National Key R&D Program of China (Nos. 2017YFA0403301 and 2018YFB0504302), and the Key Program of CAS (No. XDB17030500).

X-ray ghost imaging (XGI) has opened up a new avenue for damage-free medical imaging. Here energy-selective spectroscopic XGI under poor illumination is demonstrated with a single-pixel detector for the first time. The key device was a specially fabricated Au mask incorporating a new modulation pattern design, by which means images of a real object were obtained with a spatial resolution of 10 μm and a spectral energy resolution of about 1.5 keV. Compressed sensing was also introduced to improve the image quality. Our proof-of-principle experiment extends the methodology of XGI to make possible the retrieval of spectral images with only a single-pixel detector, and paves the way for potential applications in many fields such as biology, material science and environmental sensing.

A single-pixel hyperspectral imager using two-stage Hadamard encoding

This paper presents a hyperspectral imaging approach using a single-pixel detector. The system consists of a two-stage encoding system based on the concept of Hadamard transform. In contrast with conventional hyperspectral imaging methods where wavelength filters or Fourier spectroscopy is usually adopted, the spectral imaging system in this design uses an encoded slit, a resonant scanning mirror, and a fixed encoder mask to reconstruct the spectral image. The proposed method provides a novel way to set up an easily-controllable single-pixel approach that allows the imager to operate at any wavelength. We present a complete single-pixel hyperspectral imaging system with experimental results to demonstrate its working performance.

Performance analysis optimization and experimental verification of a photon-counting communication system based on non-photon-number-resolution detectors

With the increase in free-space optical communication distance and link attenuation, photon-counting communication based on a single photon detector will be the development trend in the future. In this paper, we use a single-pixel single photon avalanche diode (SPAD) detector to study the bit error rate (BER) performance of photon counting communication. As the single-pixel SPAD cannot distinguish the number of photons in a single detection event, we theoretically derive the (BER) model of a pulse position modulation (PPM) communication system based on a single-pixel SPAD detector. Then, the binary asymmetric channel (BAC) model is adopted to modify the channel log-likelihood in Serially Concatenated Pulse Position Modulation (SCPPM) decoding. Research results show that, when the BER is 10−6 , the photon efficiency can reach 1.6 photon per bit (PPB) in the modified BAC scheme with 0.1 photon/slot noise intensity, which is 28.6% better than the photon efficiency of 2.24 PPB in the unmodified Poisson channel scheme.

Sinusoidal Single-Pixel Imaging Based on Fourier Positive–Negative Intensity Correlation

Single-pixel imaging techniques extend the time dimension to reconstruct a target scene in the spatial domain based on single-pixel detectors. Structured light illumination modulates the target scene by utilizing multi-pattern projection, and the reflected or transmitted light is measured by a single-pixel detector as total intensity. To reduce the imaging time and capture high-quality images with a single-pixel imaging technique, orthogonal patterns have been used instead of random patterns in recent years. The most representative among them are Hadamard patterns and Fourier sinusoidal patterns. Here, we present an alternative Fourier single-pixel imaging technique that can reconstruct high-quality images with an intensity correlation algorithm using acquired Fourier positive–negative images. We use the Fourier matrix to generate sinusoidal and phase-shifting sinusoid-modulated structural illumination patterns, which correspond to Fourier negative imaging and positive imaging, respectively. The proposed technique can obtain two centrosymmetric images in the intermediate imaging course. A high-quality image is reconstructed by applying intensity correlation to the negative and positive images for phase compensation. We performed simulations and experiments, which obtained high-quality images, demonstrating the feasibility of the methods. The proposed technique has the potential to image under sub-sampling conditions.

Embedding and transmitting multi-dimensional optical information through noisy environments using a single-pixel detector

Fourier single-pixel imaging (FSPI) technique can encode a real two-dimensional scene to one-dimensional time-varying light intensity sequences traveling in the free space. In this work, FSPI technique with weighted light source is regarded as a carrier of multi-dimensional information while capturing the object scene using a lens-free photodetector, and can carry visible and invisible optical information simultaneously utilizing a novel spectrum division multiplexing technique. A wavelength division multiplexing technique is adopted to improve the channel capacity as well, in which a total of six watermarks are embedded once. More importantly, our scheme successfully demonstrates that triple information (host scene, visible, and invisible optical information) can be transmitted and retrieved with high fidelity in four noisy environments including external attack, environmental interference light, thick and dynamic scattering.

Instant single-pixel imaging: on-chip real-time implementation based on the instant ghost imaging algorithm

Single-pixel imaging (SPI) uses a single-pixel detector to create an image of an object. SPI relies on a computer to construct an image, thus increasing both the size and cost of SPI and limiting its application. We developed instant single-pixel imaging (ISPI), an on-chip SPI system that implements real-time imaging at a rate of 25 fps. ISPI uses the instant ghost imaging algorithm we proposed, which leverages signal differences for image creation. It does not require a computer, which greatly reduces its both cost and size. The reconstruct time of ISPI for image creation is almost zero because little processing is required after signal detection. ISPI paves the way for the practical application of SPI.

Imaging high-speed moving targets with a single-pixel detector.

Single-pixel imaging (SPI) has recently been intensively studied as an alternative to the traditional focal plane array (FPA) technology. However, limited by the refresh rate of spatial light modulators (SLM) and inherent reconstruction mechanism, SPI is inappropriate for high-speed moving targets. To break through this limitation, we propose a novel SPI scheme for high-speed moving targets. In our scenario, the spatial encoding for the target is done by the movement of the target relative to a static pseudo-random illumination pattern. In this process, a series of single-pixel signals are generated that corresponds to the overlap between the target and certain parts of the illumination structure. This correspondence can be utilized for image reconstruction in the same way as normal SPI. In addition, compressive sensing and deep learning algorithms are used for reconstruction, respectively. Reasonable reconstructions can be obtained with a sampling ratio of only 6%. Experimental verification together with theoretical analysis has shown that our scheme is able to image high-speed moving targets that could be alternatively achieved by a fast FPA camera. Our scheme keeps the inherent advantages of SPI and meanwhile extend its application to moving targets. It is believed that this technology will have wide application in many situations.

Visual cryptography in single-pixel imaging.

Two novel visual cryptography (VC) schemes are proposed by combining VC with single-pixel imaging (SPI) for the first time. It is pointed out that the overlapping of visual key images in VC is similar to the superposition of pixel intensities by a single-pixel detector in SPI. In the first scheme, QR-code VC is designed by using opaque sheets instead of transparent sheets. The secret image can be recovered when identical illumination patterns are projected onto multiple visual key images and a single detector is used to record the total light intensities. In the second scheme, the secret image is shared by multiple illumination pattern sequences and it can be recovered when the visual key patterns are projected onto identical items. The application of VC can be extended to more diversified scenarios by our proposed schemes.

Encrypted wide-field two-photon microscopy with single-pixel detection and compressed sensing

We demonstrate a single-pixel two-photon microscopy using a compact femtosecond fiber laser by spatially tailoring the beam into orthogonal basis for patterned illumination. Such wide-field illumination excites a weak two-photon fluorescence signal that can be detected by a photomultiplier tube. An encrypted hybrid basis with random element sequence of Hadamard basis is adopted to illuminate the sample. The hybrid basis shares the same differential detection with Hadamard basis, and greatly reduces the number of measurements compared with random basis. The reduced number of measurements was demonstrated by using compressed sensing, which allows the minimum image collection and transfer bandwidth.

Single-Pixel MEMS Imaging Systems.

Single-pixel imaging technology is an attractive technology considering the increasing demand of imagers that can operate in wavelengths where traditional cameras have limited efficiency. Meanwhile, the miniaturization of imaging systems is also desired to build affordable and portable devices for field applications. Therefore, single-pixel imaging systems based on microelectromechanical systems (MEMS) is an effective solution to develop truly miniaturized imagers, owing to their ability to integrate multiple functionalities within a small device. MEMS-based single-pixel imaging systems have mainly been explored in two research directions, namely the encoding-based approach and the scanning-based approach. The scanning method utilizes a variety of MEMS scanners to scan the target scenery and has potential applications in the biological imaging field. The encoding-based system typically employs MEMS modulators and a single-pixel detector to encode the light intensities of the scenery, and the images are constructed by harvesting the power of computational technology. This has the capability to capture non-visible images and 3D images. Thus, this review discusses the two approaches in detail, and their applications are also reviewed to evaluate the efficiency and advantages in various fields.