Single-pixel Imaging Research Articles

Compact single-band and multiband terahertz plasmonic absorbers using hybrid graphene-metal resonators with switching and modulation capability

THz thermal and single-pixel imaging need compact tunable absorbers with features like high modulation depth and multi bandwidth in the case of multispectral imaging. Here we are going to propose compact switchable absorbers based on a hybrid graphene-metal resonator that is designed through a genetic algorithm and numerical method. The proposed absorbers have a thickness as small as λmax/120 and can acquire perfect modulation depth. The special shape of the proposed resonator helps to gather 6 resonance bands in a very compact unit-cell. A theoretical formula is proposed to predict the resonance frequencies of the absorber based on Fabry-Perot concepts and graphene-metal plasmons. The role of local and propagating surface plasmon and their hybridization in the absorption mechanism is clarified. The modulation capabilities and tunability of the absorbers are analyzed. The absorbers work very well in a wide range of incident angles. Based on numerical analysis the hybrid resonators were capable of suppressing parasitic resonance modes making the designed absorbers ideal for emitter applications. The proposed absorbers have applications in THz thermal imaging, modulators, and emitters.

Learning-based adaptive under-sampling for Fourier single-pixel imaging.

In this Letter, we present a learning-based method for efficient Fourier single-pixel imaging (FSI). Based on the auto-encoder, the proposed adaptive under-sampling technique (AuSamNet) manages to optimize a sampling mask and a deep neural network at the same time to achieve both under-sampling of the object image's Fourier spectrum and high-quality reconstruction from the under-sampled measurements. It is thus helpful in determining the best encoding and decoding scheme for FSI. Simulation and experiments demonstrate that AuSamNet can reconstruct high-quality natural color images even when the sampling ratio is as low as 7.5%. The proposed adaptive under-sampling strategy can be used for other computational imaging modalities, such as tomography and ptychography. We have released our source code.

Microscopic single-pixel polarimetry for biological tissue

Single-pixel imaging (SPI) is a computational imaging modality that reconstructs images from information collected with a single-pixel detector. Due to the relatively large bandwidth and cheap price of single-pixel detectors over pixel-array detectors, SPI has been demonstrated with great success in many imaging scenarios. In this work, we further extended SPI to microscopic Mueller-matrix polarimetry, which is capable of providing polarization-sensitive properties of biological tissue with a millimeter-size field of view and micrometer-size resolution. As a proof of concept, we applied polarimetry on normal and cancerous esophagus tissues from patients and reconstructed their Mueller matrices. Effects of depolarization, retardation, and diattenuation were also examined by decomposing Mueller matrices. We envision that the developed computational technique can bring new possibilities for both SPI and Mueller-matrix polarimetry.

Efficient Fourier single-pixel imaging based on weighted sorting.

Fourier single-pixel imaging (FSI) has attracted increased attention in recent years with the advantages of a wide spectrum range and low cost. FSI reconstructs a scene by directly measuring the Fourier coefficients with a single-pixel detector. However, the existing sampling method is difficult to balance the noise suppression and image details within a limited number of measurements. Here we propose a new sampling strategy for FSI to solve this problem. Both the generality of the spectral distribution of natural images in the Fourier domain and the uniqueness of the spectral distribution of the target images in the Fourier domain are considered in the proposed method. These two distributions are summed with certain weights to determine the importance of the Fourier coefficients. Then these coefficients are sampled in order of decreasing importance. Both the simulations and experiments demonstrate that the proposed method can capture more key Fourier coefficients and retain more details with lower noise. The proposed method provides an efficient way for Fourier coefficient acquisition.

A Single-Pixel Imaging Method Based on Compressed Sensing for Improvement of Image Quality

The image quality under single-pixel imaging method combined with compressed sensing theory is poor at low sampling rates. To solve this problem, we propose a new sparse matrix and a new imaging method. First, we construct truncated sparse wavelet transform basis based on discrete wavelet transform basis by using the construction method of sparse wavelet transform basis, so that the signal is represented more sparsely. After that, we optimize ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm minimization algorithm by constructing an intermediate function so that the optimal solution of transformed problem is closer to the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm minimum solution. Simulation and experiment show that the proposed method increases peak signal-to-noise ratio and structural similarity of imaging results, effectively improving the image quality.

Design and analysis of polygonal mirror-based scan engines for improved spatial frequency modulation imaging.

Spatial frequency modulation imaging (SPIFI) is a structured illumination single pixel imaging technique that is most often achieved via a rotating modulation disk. This implementation produces line images with exposure times on the order of tens of milliseconds. Here, we present a new architecture for SPIFI using a polygonal scan mirror with the following advances: (1) reducing SPIFI line image exposure times by 2 orders of magnitude, (2) facet-to-facet measurement and correction for polygonal scan design, and (3) a new anamorphic magnification scheme that improves resolution for long working distance optics.

Comparison of Common Algorithms for Single-Pixel Imaging via Compressed Sensing.

Single-pixel imaging (SPI) uses a single-pixel detector instead of a detector array with a lot of pixels in traditional imaging techniques to realize two-dimensional or even multi-dimensional imaging. For SPI using compressed sensing, the target to be imaged is illuminated by a series of patterns with spatial resolution, and then the reflected or transmitted intensity is compressively sampled by the single-pixel detector to reconstruct the target image while breaking the limitation of the Nyquist sampling theorem. Recently, in the area of signal processing using compressed sensing, many measurement matrices as well as reconstruction algorithms have been proposed. It is necessary to explore the application of these methods in SPI. Therefore, this paper reviews the concept of compressive sensing SPI and summarizes the main measurement matrices and reconstruction algorithms in compressive sensing. Further, the performance of their applications in SPI through simulations and experiments is explored in detail, and then their advantages and disadvantages are summarized. Finally, the prospect of compressive sensing with SPI is discussed.

Local-enhanced transformer for single-pixel imaging.

Deep learning networks have been applied to under-sampled single-pixel imaging (SPI) for better reconstruction performance. However, the existing deep-learning-based SPI methods with convolutional filters have difficulty in modeling long-range dependencies of SPI measurements and thus show limited reconstruction quality. Recently, the transformer has demonstrated great potential in capturing long-range dependencies, but it lacks locality mechanism and thus could be sub-optimal when directly used for under-sampled SPI. In this Letter, we propose a high-quality under-sampled SPI method based on a novel, to the best of our knowledge, local-enhanced transformer. The proposed local-enhanced transformer is not only good at capturing global dependencies of SPI measurements, but also has the capability to model local dependencies. Additionally, the proposed method employs optimal binary patterns, which makes the sampling high-efficiency and hardware-friendly. Experiments on simulated data and real measured data demonstrate that our proposed method outperforms the state-of-the-art SPI methods.

Wavefront single-pixel imaging using a flexible SLM-based common-path interferometer

Wavefront single-pixel imaging (WSPI) is an important tool for simultaneously measuring the amplitude and phase of an unknown field, especially suitable for wavefront detecting in some special wavelengths where array detectors are immature or even unavailable. Here, a passive-detection-based WSPI method using a flexible SLM-based common-path interferometer is proposed, in which by detecting the zero-order diffraction of the SLM directly, the common-path phase-shift or off-axis technique can be flexibly realized by a “sandwich” structure consisting of a half-wave plate, a SLM, and a polarizer. The reference light can be conveniently chosen as the direct reflected light from the SLM, the unmodulated light from the pixels of the SLM, or the light from both of them. We experimentally verify the feasibility and advantages of the proposed method. We further demonstrate the method for wavefront measurement of a dragonfly wing, which suggests it can be used for quantitative phase imaging. It provides a high-resolution spatial wavefront passive detection technique, with a compact, stable, and flexible setup, which has lots of applications involving three-dimensional imaging, aberration correction, digital holographic measurement, and digital microscopy.

Polarization-Dependent Metasurface Enables Near-Infrared Dual-Modal Single-Pixel Sensing.

Infrared single-pixel sensing with the two most representative modes, bright-field imaging and edge-enhanced imaging, has great application potential in biomedical diagnosis and defect inspection. Building a multifunctional and miniature optical computing device for infrared single-pixel sensing is extremely intriguing. Here, we propose and validate a dual-modal device based on a well-designed metasurface, which enables near-infrared bright-field and edge-enhanced single-pixel imaging. By changing the polarization of the incident beam, these two different modes can be switched. Simulations validate that our device can achieve high-fidelity dual-modal single-pixel sensing at 0.9 μm with certain noise robustness. We also investigate the generalization of our metasurface-based device and validate that different illumination patterns are applied to our device. Moreover, these output images by our device can be efficiently utilized for biomedical image segmentation. We envision this novel device may open a vista in dual-modal infrared single-pixel sensing.

OpenSpyrit: an ecosystem for open single-pixel hyperspectral imaging.

This paper describes OpenSpyrit, an open access and open source ecosystem for reproducible research in hyperspectral single-pixel imaging, composed of SPAS (a Python single-pixel acquisition software), SPYRIT (a Python single-pixel reconstruction toolkit) and SPIHIM (a single-pixel hyperspectral image collection). The proposed OpenSpyrit ecosystem responds to the need for reproducibility and benchmarking in single-pixel imaging by providing open data and open software. The SPIHIM collection, which is the first open-access FAIR dataset for hyperspectral single-pixel imaging, currently includes 140 raw measurements acquired using SPAS and the corresponding hypercubes reconstructed using SPYRIT. The hypercubes are reconstructed by both inverse Hadamard transformation of the raw data and using the denoised completion network (DC-Net), a data-driven reconstruction algorithm. The hypercubes obtained by inverse Hadamard transformation have a native size of 64 × 64 × 2048 for a spectral resolution of 2.3 nm and a spatial resolution that is comprised between 182.4 µm and 15.2 µm depending on the digital zoom. The hypercubes obtained using the DC-Net are reconstructed at an increased resolution of 128 × 128 × 2048. The OpenSpyrit ecosystem should constitute a reference to support benchmarking for future developments in single-pixel imaging.

Resolution-enhanced single-pixel imaging using the Hadamard transform matrix

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.

Single-Pixel Imaging in Space and Time with Optically Modulated Free Electrons.

Single-pixel imaging, originally developed in light optics, facilitates fast three-dimensional sample reconstruction as well as probing with light wavelengths undetectable by conventional multi-pixel detectors. However, the spatial resolution of optics-based single-pixel microscopy is limited by diffraction to hundreds of nanometers. Here, we propose an implementation of single-pixel imaging relying on attainable modifications of currently available ultrafast electron microscopes in which optically modulated electrons are used instead of photons to achieve subnanometer spatially and temporally resolved single-pixel imaging. We simulate electron beam profiles generated by interaction with the optical field produced by an externally programmable spatial light modulator and demonstrate the feasibility of the method by showing that the sample image and its temporal evolution can be reconstructed using realistic imperfect illumination patterns. Electron single-pixel imaging holds strong potential for application in low-dose probing of beam-sensitive biological and molecular samples, including rapid screening during in situ experiments.

Single photon single pixel imaging into thick scattering medium.

Imaging into thick scattering medium is a long-standing challenge. Beyond the quasi-ballistic regime, multiple scattering scrambles the spatiotemporal information of incident/emitted light, making canonical imaging based on light focusing nearly impossible. Diffusion optical tomography (DOT) is one of the most popular approach to look inside scattering medium, but quantitatively inverting the diffusion equation is ill-posed, and prior information of the medium is typically necessary, which is nontrivial to obtain. Here, we show theoretically and experimentally that, by synergizing the one-way light scattering characteristic of single pixel imaging with ultrasensitive single photon detection and a metric-guided image reconstruction, single photon single pixel imaging can serve as a simple and powerful alternative to DOT for imaging into thick scattering medium without prior knowledge or inverting the diffusion equation. We demonstrated an image resolution of 12 mm inside a 60 mm thick (∼ 78 mean free paths) scattering medium.

Feature single-pixel imaging: What you see is what you want

Traditional imaging technology is basically a “what you see is what you get” imaging method, providing images that resemble human vision for post-information acquisition and interpretation. This Letter proposes a feature single-pixel imaging technique that allows for direct imaging of specific features without the need for traditional image processing methods. This approach enables the imaging of a single feature at a remarkable ultra-low sampling ratio of 2% with a resolution of up to 128 × 128 pixels, operating at a frequency of 67 Hz, even in dynamic environments where the location of the target or background features may change. Additionally, the proposed method demonstrates the ability to selectively image defects on an integrated circuit wafer. This research offers a significant advancement in the development of single-pixel imaging for feature information acquisition and has immense potential for applications in various industries and daily life scenarios.

Fourier Single-Pixel Imaging Via Arbitrary Illumination Patterns

The traditional Fourier single-pixel imaging (FSI) utilizes cosinusoidal (grayscale) illumination patterns to reconstruct the image of an object. In this paper, we propose a universal mechanism in which FSI can be implemented via arbitrary periodic structured illumination not merely cosinusoidal illumination patterns. The proposed theory is verified by numerical simulations and optical experiments. In the experiment, we demonstrate the result of FSI with phase-shifting sawtooth (trapezoid, square) structured illumination modulated by a light projector. Moreover, in order to underline the high efficiency of the proposed mechanism, we perform FSI utilizing binary (square) illumination patterns generated directly by the digital micromirror devices (DMDs). Therefore, this mechanism can promote the high-performance and extensive applications of FSI in various scenarios.

Spatial temporal Fourier single-pixel imaging.

Generally, the imaging quality of Fourier single-pixel imaging (FSI) will severely degrade while achieving high-speed imaging at a low sampling rate (SR). To tackle this problem, a new, to the best of our knowledge, imaging technique is proposed: firstly, the Hessian-based norm constraint is introduced to deal with the staircase effect caused by the low SR and total variation regularization; secondly, based on the local similarity prior of consecutive frames in the time dimension, we designed the temporal local image low-rank constraint for the FSI, and combined the spatiotemporal random sampling method, the redundancy image information of consecutive frames can be utilized sufficiently; finally, by introducing additional variables to decompose the optimization problem into multiple sub-problems and analytically solving each one, a closed-form algorithm is derived for efficient image reconstruction. Experimental results show that the proposed method improves imaging quality significantly compared with state-of-the-art methods.

Image-free active autofocusing with dual modulation and its application to Fourier single-pixel imaging.

Autofocusing is widely used in applications where sharp image acquisition or projection is needed. Here we report an active autofocusing method for sharp image projection. The method works with wide-field structured illumination and single-pixel detection. To find the focus position, the method illuminates the target object with a set of 3-step phase-shifting Fourier basis patterns repeatedly and collects the backscattered light by using a single-pixel detector through a grating. Dual modulation-dynamic modulation by the time-varying structured illumination and static modulation by the grating-embeds the depth information for the target object in the resulting single-pixel measurements. As such, the focus position can be determined by recovering the Fourier coefficients from the single-pixel measurements and searching for the coefficient with the maximum magnitude. High-speed spatial light modulation not only enables rapid autofocusing but also makes the method work even when the lens system is in continuous motion or the focal length of the lens is continuously adjusted. We experimentally validate the reported method in a self-built digital projector and demonstrate the application of the method in Fourier single-pixel imaging.

Hadamard Single-Pixel Imaging Based on Positive Patterns

Hadamard single-pixel imaging (SPI) employs the differential measurement strategy to eliminate the effect of negative value of Hadamard basis patterns but leads to doubling the number of measurements. To reduce the number of measurements, a Hadamard SPI method based on positive patterns is proposed. In this method, only the positive patterns are used to acquire measurement values and reconstruct images, so the number of measurements will be reduced by 1/2. Combined with the intensity correlation theory of ghost imaging, the average value of the acquired measures is found; this average value is subtracted from all the measurement values to obtain the spectral coefficients, thus the background noise is eliminated to ensure the imaging quality. Simulation and experimental results show that the proposed method has good noise robustness and can efficiently reconstruct high quality images.

OAM-Basis Wavefront Single-Pixel Imaging via Compressed Sensing

Utilizing a complex-number basis for single-pixel imaging (SPI) a spatial wavefront in high quality is still an open issue. To solve this issue, here, we proposed a method called OAM-basis spatial wavefront single-pixel imaging (WSPI) via compressed sensing (CS) for reconstructing a spatial wavefront in high quality. In there, we generate the orbital angular momentum (OAM) beams as the complex-number basis for the measurement, and propose to use the TVAL3 CS algorithm for the wavefront reconstruction, for the first time to the best of our knowledge. The reconstruction method is appropriate for WSPI using a complex-number basis. Both simulations and experiments show that our method is superior to the existing second-order correlation method. It provides a promising complex-number-basis-based WSPI technique for high-quality wavefront reconstruction. Thus, it may find applications involving optical communication, optical detection and imaging, optical information processing and encryption, and so on.