Single pixel imaging at megahertz switching rates via cyclic Hadamard masks

Deep compressed multichannel adaptive optics scanning light ophthalmoscope

Adaptive optics scanning light ophthalmoscopy (AOSLO) reveals individual retinal cells and their function, microvasculature, and micropathologies in vivo. As compared to the single-channel offset pinhole and two-channel split-detector nonconfocal AOSLO designs, by providing multidirectional imaging capabilities, a recent generation of multidetector and (multi-)offset aperture AOSLO modalities has been demonstrated to provide critical information about retinal microstructures. However, increasing detection channels requires expensive optical components and/or critically increases imaging time. To address this issue, we present an innovative combination of machine learning and optics as an integrated technology to compressively capture 12 nonconfocal channel AOSLO images simultaneously. Imaging of healthy participants and diseased subjects using the proposed deep compressed multichannel AOSLO showed enhanced visualization of rods, cones, and mural cells with over an order-of-magnitude improvement in imaging speed as compared to conventional offset aperture imaging. To facilitate the adaptation and integration with other in vivo microscopy systems, we made optical design, acquisition, and computational reconstruction codes open source.

Physical twinning for joint encoding-decoding optimization in computational optics: a review

Computational optics introduces computation into optics and consequently helps overcome traditional optical limitations such as low sensing dimension, low light throughput, low resolution, and so on. The combination of optical encoding and computational decoding offers enhanced imaging and sensing capabilities with diverse applications in biomedicine, astronomy, agriculture, etc. With the great advance of artificial intelligence in the last decade, deep learning has further boosted computational optics with higher precision and efficiency. Recently, there developed an end-to-end joint optimization technique that digitally twins optical encoding to neural network layers, and then facilitates simultaneous optimization with the decoding process. This framework offers effective performance enhancement over conventional techniques. However, the reverse physical twinning from optimized encoding parameters to practical modulation elements faces a serious challenge, due to the discrepant gap in such as bit depth, numerical range, and stability. In this regard, this review explores various optical modulation elements across spatial, phase, and spectral dimensions in the digital twin model for joint encoding-decoding optimization. Our analysis offers constructive guidance for finding the most appropriate modulation element in diverse imaging and sensing tasks concerning various requirements of precision, speed, and robustness. The review may help tackle the above twinning challenge and pave the way for next-generation computational optics.

Fourier single-pixel spectral imaging via local low-rank tensor nuclear norm and deep tensor priors

The imaging quality of single-pixel spectral imaging (SSI) methods is poor at a low sampling ratio (SR). To tackle this problem, a new Fourier single-pixel spectral imaging (FSSI) technique is proposed. Firstly, we introduce the low-rank tensor nuclear norm (TNN) to characterize the correlation between spectral images. Compared with the conventional method, TNN reconstructs image details better but brings image artifacts simultaneously. Therefore, local low-rank TNN (LTNN) constraint is proposed to ameliorate global ones and to reduce the distortion caused by TNN and low SR. Secondly, to make full use of the spectral information, the proposed constraint is used as the coarse prior, and the deep tensor prior (DTP) is introduced as the fine one to construct the joint priors. Different from the single prior, the joint method can make the two priors benefit and improve each other and further enhance the imaging quality. Finally, an efficient and high-quality SSI technique is achieved by deducing the closed-form solution algorithm. Experimental results show that our method significantly improves the quality of FSSI as much as 7-10 dB when compared to 3DTV at the SR of 5%.

Photon-counting single-pixel camera based on a fast spinning coding disk

Spinning coding masks, recognized for their fast modulation rate and cost-effectiveness, are now often used in real-time single-pixel imaging (SPI). However, in the photon-counting regime, they encounter difficulties in synchronization between the coding mask patterns and the photon detector, unlike digital micromirror devices. To address this issue, we propose a scheme that assumes a constant disk rotation speed throughout each cycle and models photon detection as a non-homogeneous Poisson process (NHPP). This effectively resolves synchronization problems and compensates for speed fluctuations. To validate this method, we designed and fabricated a single-pixel camera prototype that can capture images under an illumination of less than one photon per pixel, with a modulation rate of approximately 100 kHz and an imaging speed of 28 frames per second. The camera is compact, lightweight, and low cost and should find many practical applications for imaging under extremely low-light conditions.

Noise-robust and data-efficient compressed ghost imaging via the preconditioned S-matrix method

The design of the illumination pattern is crucial for improving imaging quality of ghost imaging (GI). The S-matrix is an ideal binary matrix for use in GI with non-visible light and other particles since there are no uniformly configurable beam-shaping modulators in these GI regimes. However, unlike widely researched GI with visible light, there is relatively little research on the sampling rate and noise resistance of compressed GI based on the S-matrix. In this paper, we investigate the performance of compressed GI using the S-matrix as the illumination pattern (SCSGI) and propose a post-processing method called preconditioned S-matrix compressed GI (PSCSGI) to improve the imaging quality and data efficiency of SCSGI. Simulation and experimental results demonstrate that compared with SCSGI, PSCSGI can improve imaging quality in noisy conditions while utilizing only half the amount of data used in SCSGI. Furthermore, better reconstructed results can be obtained even when the sampling rate is as low as 5%. The proposed PSCSGI method is expected to advance the application of binary masks based on the S-matrix in GI.

Computational ghost imaging enhanced by degradation models for under-sampling

Computational ghost imaging (CGI) allows two-dimensional (2D) imaging by using spatial light modulators and bucket detectors. However, most CGI methods attempt to obtain 2D images through measurements with a single sampling ratio. Here, we propose a CGI method enhanced by degradation models for under-sampling, which can be reflected by results from measurements with different sampling ratios. We utilize results from low-sampling-ratio measurements and normal-sampling-ratio measurements to train the neural network for the degradation model, which is fitted through self-supervised learning. We obtain final results by importing normal-sampling-ratio results into the neural network with optimal parameters. We experimentally demonstrate improved results from the CGI method using degradation models for under-sampling. Our proposed method would promote the development of CGI in many applications.

Single-pixel microscopy with optical sectioning

Imaging with single-pixel detectors offers a valuable alternative to the conventional focal plane array strategy, especially for wavelengths where silicon-based sensor arrays exhibit lower efficiency. However, the absence of optical sectioning remains a challenge in single-pixel microscopy. In this paper, we introduce a single-pixel microscope with optical sectioning capabilities by integrating single-pixel imaging (SPI) techniques with structured illumination microscopy (SIM) methods. A spatial light modulator positioned at the microscope's input port encodes a series of structured light patterns, which the microscope focuses onto a specific plane of the 3D sample. Simultaneously, a highly sensitive bucket detector captures the light reflected by the object. Optical sectioning is achieved through a high-frequency grating positioned at the microscope's output port, which is conjugated with the spatial light modulator. Utilizing SPI reconstruction techniques and SIM algorithms, our computational microscope produces high-quality 2D images without blurred out-of-focus regions. We validate the performance of the single-pixel microscope (SPM) by measuring the axial response function and acquiring images of various 3D samples in reflected bright-field configuration. Furthermore, we demonstrate the suitability of the optical setup for single-pixel fluorescence microscopy with optical sectioning.

High speed single pixel imaging using a microLED-on-CMOS light projector

Achieving high frame-rate operation in single pixel imaging schemes normally demands significant compromises in the flexibility of the imaging system, requiring either complex optical setups or a hardware-limited pattern mask set. Here, we demonstrate a single pixel imaging capability with pattern frame-rates approaching 400 kfps with a recently developed microLED light projector and an otherwise simple optical setup. The microLED array has individually addressable pixels and can operate significantly faster than digital micromirror devices, allowing flexibility with regards to the pattern masks employed for imaging even at the fastest frame-rates. Using a full set of Hadamard or Noiselet patterns, we demonstrate 128 × 128 pixel images being generated at 7.3 fps. We generate a pattern set specifically for the light projector using deep learning tools and use these patterns to demonstrate single pixel imaging at almost 800 fps.

A compressive hyperspectral video imaging system using a single-pixel detector

Capturing fine spatial, spectral, and temporal information of the scene is highly desirable in many applications. However, recording data of such high dimensionality requires significant transmission bandwidth. Current computational imaging methods can partially address this challenge but are still limited in reducing input data throughput. In this paper, we report a video-rate hyperspectral imager based on a single-pixel photodetector which can achieve high-throughput hyperspectral video recording at a low bandwidth. We leverage the insight that 4-dimensional (4D) hyperspectral videos are considerably more compressible than 2D grayscale images. We propose a joint spatial-spectral capturing scheme encoding the scene into highly compressed measurements and obtaining temporal correlation at the same time. Furthermore, we propose a reconstruction method relying on a signal sparsity model in 4D space and a deep learning reconstruction approach greatly accelerating reconstruction. We demonstrate reconstruction of 128 × 128 hyperspectral images with 64 spectral bands at more than 4 frames per second offering a 900× data throughput compared to conventional imaging, which we believe is a first-of-its kind of a single-pixel-based hyperspectral imager.

Global-optimal semi-supervised learning for single-pixel image-free sensing

Single-pixel sensing offers low-cost detection and reliable perception, and the image-free sensing technique enhances its efficiency by extracting high-level features directly from compressed measurements. However, the conventional methods have great limitations in practical applications, due to their high dependence on large labelled data sources and incapability to do complex tasks. In this Letter, we report an image-free semi-supervised sensing framework based on GAN and achieve an end-to-end global optimization on the part-labelled datasets. Simulation on the MNIST realizes 94.91% sensing accuracy at 0.1 sampling ratio, with merely 0.3% of the dataset holding its classification label. When comparing to the conventional single-pixel sensing methods, the reported technique not only contributes to a high-robust result in both conventional (98.49% vs. 97.36%) and resource-constrained situations (94.91% vs. 83.83%) but also offers a more practical and powerful detection fashion for single-pixel sensing, with much less human effort and computation resources.

Single-pixel imaging based on metasurface fuzzy coding

Single-pixel imaging, renowned for its high sensitivity, robustness against interference, and superior resolution, has become increasingly prominent in the field of optical research. Over recent years, a diverse array of light modulation devices and methodologies has been devised to accomplish megahertz modulations rates. This work presents a single-pixel imaging scheme based on the fuzzy coding of metasurfaces. This unique encoding technique manipulates the quality of the mask pattern by adjusting the pixel count within the metasurface units. Notably, we expand the metasurface units to effectively mitigate the position sensitivity during movement or rotations, thus easing the challenge for the detector in collecting the correct light intensity during sub-mask transitions. A detailed analysis is drawn of the reconstruction quality of fuzzy masks. Simultaneously, we provide simulations of single-pixel imaging under the condition where the fuzzy-coded metasurface is moving. This work provides a new, to the best of our knowledge, mask generation mode for high-speed spatial light modulation.

Scan-less microscopy based on acousto-optic encoded illumination

Several optical microscopy methods are now available for characterizing scientific and industrial processes at sub-micron resolution. However, they are often ill-suited for imaging rapid events. Limited by the trade-off between camera frame-rate and sensitivity, or the need for mechanical scanning, current microscopes are optimized for imaging at hundreds of frames-per-second (fps), well-below what is needed in processes such as neuronal signaling or moving parts in manufacturing lines. Here, we present a scan-less technology that allows sub-micrometric imaging at thousands of fps. It is based on combining a single-pixel camera with parallelized encoded illumination. We use two acousto-optic deflectors (AODs) placed in a Mach-Zehnder interferometer and drive them simultaneously with multiple and unique acoustic frequencies. As a result, orthogonal light stripes are obtained that interfere with the sample plane, forming a two-dimensional array of flickering spots - each with its modulation frequency. The light from the sample is collected with a single photodiode that, after spectrum analysis, allows for image reconstruction at speeds only limited by the AOD's bandwidth and laser power. We describe the working principle of our approach, characterize its imaging performance as a function of the number of pixels - up to 400 × 400 - and characterize dynamic events at 5000 fps.

Optimizing the ordering of the Hadamard masks of ghost imaging suitable for the efficient face reconstruction using the max-projection method

One crucial component of ghost imaging (GI) is the encoded mask. Higher-quality reconstruction at lower sampling rates is still a major challenge for GI. Inspired by deep learning, max-projection method is proposed in the paper to reorder the Hadamard masks for its efficient and rapid reconstruction. The simulations demonstrated that max-projection ordering with only 20 face training images yielded excellent reconstruction outcomes. In noise-free simulations, at an ultralow sampling rate of 5%, the PSNR of the max-projection ordering was 1.1 dB higher than that of the cake-cutting ordering with the best performance in the reference group. In noisy simulations, at ultralow sampling rates, the retrieved images remained almost identical to their noise-free counterparts. Irrespective of the presence or absence of noise, the max-projection ordering guaranteed the highest fidelity of image reconstruction at ultralow sampling rates. The reconstruction time was reduced to mere milliseconds, thereby enabling swift visualization of dynamic phenomena. Accordingly, the max-projection ordering Hadamard matrix offers a promising solution for real-time GI due to its higher reconstruction quality, stronger noise immunity and millisecond reconstruction time.

Cascaded domain multiphoton spatial frequency modulation imaging

Significance

Multiphoton microscopy is a powerful imaging tool for biomedical applications. A variety of techniques and respective benefits exist for multiphoton microscopy, but an enhanced resolution is especially desired. Additionally multiphoton microscopy requires ultrafast pulses for excitation, so optimization of the pulse duration at the sample is critical for strong signals.

Aim

We aim to perform enhanced resolution imaging that is robust to scattering using a structured illumination technique while also providing a rapid and easily repeatable means to optimize group delay dispersion (GDD) compensation through to the sample.

Approach

Spatial frequency modulation imaging (SPIFI) is used in two domains: the spatial domain (SD) and the wavelength domain (WD). The WD-SPIFI system is an in-line tool enabling GDD optimization that considers all material through to the sample. The SD-SPIFI system follows and enables enhanced resolution imaging.

Results

The WD-SPIFI dispersion optimization performance is confirmed with independent pulse characterization, enabling rapid optimization of pulses for imaging with the SD-SPIFI system. The SD-SPIFI system demonstrates enhanced resolution imaging without the use of photon counting enabled by signal to noise improvements due to the WD-SPIFI system.

Conclusions

Implementing SPIFI in-line in two domains enables full-path dispersion compensation optimization through to the sample for enhanced resolution multiphoton microscopy.

High-resolution self-corrected single-pixel imaging through dynamic and complex scattering media

Imaging with single-pixel detectors becomes attractive in many applications where pixelated detectors are not available or cannot work. Based on a correlation between the probing patterns and the realizations, optical imaging with single-pixel detector offers an indirect way to recover a sample. It is well recognized that single-pixel optical imaging through dynamic and complex scattering media is challenging, and dynamic scaling factors lead to serious mismatches between the probing patterns and the realizations. In this paper, we report self-corrected imaging to realize high-resolution object reconstruction through dynamic and complex scattering media using a parallel detection with dual single-pixel detectors. The proposed method can supervise and self-correct dynamic scaling factors, and can implement high-resolution object reconstruction through dynamic and complex scattering media where conventional methods could not work. Spatial resolution of 44.19 µm is achieved which approaches diffraction limit (40.0 µm) in the designed optical setup. The achievable spatial resolution is dependent on pixel size of spatial light modulator. It is experimentally validated that the proposed method shows unprecedented robustness against complex scattering. The proposed self-corrected imaging provides a solution for ghost recovery, enabling high-resolution object reconstruction in complex scattering environments.

High-efficiency terahertz single-pixel imaging based on a physics-enhanced network

As an alternative solution to the lack of cost-effective multipixel terahertz cameras, terahertz single-pixel imaging that is free from pixel-by-pixel mechanical scanning has been attracting increasing attention. Such a technique relies on illuminating the object with a series of spatial light patterns and recording with a single-pixel detector for each one of them. This leads to a trade-off between the acquisition time and the image quality, hindering practical applications. Here, we tackle this challenge and demonstrate high-efficiency terahertz single-pixel imaging based on physically enhanced deep learning networks for both pattern generation and image reconstruction. Simulation and experimental results show that this strategy is much more efficient than the classical terahertz single-pixel imaging methods based on Hadamard or Fourier patterns, and can reconstruct high-quality terahertz images with a significantly reduced number of measurements, corresponding to an ultra-low sampling ratio down to 1.56%. The efficiency, robustness and generalization of the developed approach are also experimentally validated using different types of objects and different image resolutions, and clear image reconstruction with a low sampling ratio of 3.12% is demonstrated. The developed method speeds up the terahertz single-pixel imaging while reserving high image quality, and advances its real-time applications in security, industry, and scientific research.

Mid-infrared single-pixel imaging at the single-photon level

Single-pixel cameras have recently emerged as promising alternatives to multi-pixel sensors due to reduced costs and superior durability, which are particularly attractive for mid-infrared (MIR) imaging pertinent to applications including industry inspection and biomedical diagnosis. To date, MIR single-pixel photon-sparse imaging has yet been realized, which urgently calls for high-sensitivity optical detectors and high-fidelity spatial modulators. Here, we demonstrate a MIR single-photon computational imaging with a single-element silicon detector. The underlying methodology relies on nonlinear structured detection, where encoded time-varying pump patterns are optically imprinted onto a MIR object image through sum-frequency generation. Simultaneously, the MIR radiation is spectrally translated into the visible region, thus permitting infrared single-photon upconversion detection. Then, the use of advanced algorithms of compressed sensing and deep learning allows us to reconstruct MIR images under sub-Nyquist sampling and photon-starving illumination. The presented paradigm of single-pixel upconversion imaging is featured with single-pixel simplicity, single-photon sensitivity, and room-temperature operation, which would establish a new path for sensitive imaging at longer infrared wavelengths or terahertz frequencies, where high-sensitivity photon counters and high-fidelity spatial modulators are typically hard to access.

Spectrometer-based wavelength interrogation SPR imaging via Hadamard transform

We present spectrometer-based wavelength interrogation surface plasmon resonance imaging (SPRi) without mechanical scanning. A polarized broadband light source illuminates an object via a gold-coated prism; the reflected light is spatially modulated by a digital mirror device (DMD) and then measured with a spectrometer. Reflectance spectral images are reconstructed via the Hadamard transform (HT), and a refractive index (RI) map is visualized from the reflectance spectral images by analyzing the resonance peak shift of the spectrum at each image pixel. We demonstrate the feasibility of our method by evaluating the resolution, sensitivity, and dynamic detection range, experimentally obtained as ∼2.203 × 10^-6 RI unit (RIU), ∼3,407 nm/RIU, and ∼0.1403 RIU, respectively. Furthermore, simulations are performed to validate the experimental results.

Single-pixel imaging of a translational object

Image-free tracking methods based on single-pixel detectors (SPDs) can track a moving object at a very high frame rate, but they rarely can achieve simultaneous imaging of such an object. In this study, we propose a method for simultaneously obtaining the relative displacements and images of a translational object. Four binary Fourier patterns and two differential Hadamard patterns are used to modulate one frame of the object and then modulated light signals are obtained by SPD. The relative displacements and image of the moving object can be gradually obtained along with the detection. The proposed method does not require any prior knowledge of the object and its motion. The method has been verified by simulations and experiments, achieving a frame rate of 3332 Hz to acquire relative displacements of a translational object at a spatial resolution of 128 × 128 pixels using a 20000-Hz digital micro-mirror device. This proposed method can broaden the application of image-free tracking methods and obtain spatial information about moving objects.

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.

Compressed ultrahigh-speed single-pixel imaging by swept aggregate patterns

Single-pixel imaging (SPI) has emerged as a powerful technique that uses coded wide-field illumination with sampling by a single-point detector. Most SPI systems are limited by the refresh rates of digital micromirror devices (DMDs) and time-consuming iterations in compressed-sensing (CS)-based reconstruction. Recent efforts in overcoming the speed limit in SPI, such as the use of fast-moving mechanical masks, suffer from low reconfigurability and/or reduced accuracy. To address these challenges, we develop SPI accelerated via swept aggregate patterns (SPI-ASAP) that combines a DMD with laser scanning hardware to achieve pattern projection rates of up to 14.1 MHz and tunable frame sizes of up to 101×103 pixels. Meanwhile, leveraging the structural properties of S-cyclic matrices, a lightweight CS reconstruction algorithm, fully compatible with parallel computing, is developed for real-time video streaming at 100 frames per second (fps). SPI-ASAP allows reconfigurable imaging in both transmission and reflection modes, dynamic imaging under strong ambient light, and offline ultrahigh-speed imaging at speeds of up to 12,000 fps.

Single-pixel imaging of a randomly moving object

Single-pixel imaging enjoys advantages of low budget, broad spectrum, and high imaging speed. However, existing methods cannot clearly reconstruct the object that is fast rotating or randomly moving. In this work, we put forward an effective method to image a randomly moving object based on geometric moment analysis. To the best of our knowledge, this is the first work that reconstructs the shape and motion state of the target without prior knowledge of the speed or position. By using the cake-cutting order Hadamard illumination patterns and low-order geometric moment patterns, we obtain a high-quality video stream of the target which moves at high and varying translational and rotational speeds. The efficient method as verified by simulation and experimental results has great potential for practical applications such as Brownian motion microscopy and remote sensing.

Single-pixel imaging with Gao-Boole patterns

Single-pixel imaging (SPI) can perceive the world using only a single-pixel detector, but long sampling times with a series of patterns are inevitable for SPI, which is the bottleneck for its practical application. Developing new patterns to reduce the sampling times might provide opportunities to address this challenge. Based on the Kronecker product of Hadamard matrix, we here design a complete set of new patterns, called Gao-Boole patterns, for SPI. Compared to orthogonal Hadamard basis patterns with elements valued as +1 or -1, our Gao-Boole patterns are non-orthogonal ones and the element values are designed as +1 or 0. Using our Gao-Boole patterns, the reconstructed quality of a target image (N × N pixels) is as high as the Hadamard one but only with half pattern numbers of the Hadamard ones, for both full sampling (N² for Gao-Boole patterns, 2N² for Hadamard basis patterns) and undersampling cases in experiment. Effectively reducing the patterns numbers and sampling times without sacrificing imaging quality, our designed Gao-Boole patterns provide a superior option for structural patterns in SPI and help to steer SPI toward practical imaging application.

Sequency encoding single pixel spectroscopy based on Hadamard transform

Single pixel spectroscopy based on Hadamard transform (SPS-HT) has been applied widely because of its capability of wavelength multiplexing and associated advantage in signal-to-noise ratio. In this paper, we propose a sequency encoding single pixel spectroscopy (SESPS) based on two-dimensional (2D) masks for concurrent coding of all Hadamard coefficients instead of one-dimensional (1D) Hadamard masks (only coding one coefficient at a time) widely used in the traditional SPS-HT. Moreover, each Hadamard coefficient is coded along the time dimension with a different sequency value such that the alternating current (AC) measurements of the time-domain signal can be used to reconstruct all Hadamard coefficients simultaneously, which reduces the influence of noise and dramatically speeds up data acquisition. We demonstrate that the SESPS with 32 spectral channels can accelerate spectral measurements from white light sources and fluorescence particles by around 14 times and 70 times, respectively, compared to measurements using a commercial spectrometer when the relative root mean square error (RMSE) is around 3% or smaller. The acceleration factors can be boosted by an extra 4 times when only eight spectral channels are used to achieve a compression ratio of 4:1, in which the relative RMSEs change only marginally. Compared to our previous SPS-HT, this new scheme can increase the speed by three orders of magnitude. This technique is expected to be useful in applications requiring high-speed spectral measurements such as the spectral flow cytometry and on-site medical diagnosis using fluorescence or Raman spectroscopy.

Gigahertz-rate random speckle projection for high-speed single-pixel image classification

Imaging techniques based on single-pixel detection, such as ghost imaging, can reconstruct or recognize a target scene from multiple measurements using a sequence of random mask patterns. However, the processing speed is limited by the low rate of the pattern generation. In this study, we propose an ultrafast method for random speckle pattern generation, which has the potential to overcome the limited processing speed. The proposed approach is based on multimode fiber speckles induced by fast optical phase modulation. We experimentally demonstrate dynamic speckle projection with phase modulation at 10 GHz rates, which is five to six orders of magnitude higher than conventional modulation approaches using spatial light modulators. Moreover, we combine the proposed generation approach with a wavelength-division multiplexing technique and apply it for image classification. As a proof-of-concept demonstration, we show that 28×28-pixel images of digits acquired at GHz rates can be accurately classified using a simple neural network. The proposed approach opens a novel pathway for an all-optical image processor.

Single pixel imaging at high pixel resolutions

The usually reported pixel resolution of single pixel imaging (SPI) varies between 32 × 32 and 256 × 256 pixels falling far below imaging standards with classical methods. Low resolution results from the trade-off between the acceptable compression ratio, the limited DMD modulation frequency, and reasonable reconstruction time, and has not improved significantly during the decade of intensive research on SPI. In this paper we show that image measurement at the full resolution of the DMD, which lasts only a fraction of a second, is possible for sparse images or in a situation when the field of view is limited but is a priori unknown. We propose the sampling and reconstruction strategies that enable us to reconstruct sparse images at the resolution of 1024 × 768 within the time of 0.3s. Non-sparse images are reconstructed with less details. The compression ratio is on the order of 0.4% which corresponds to an acquisition frequency of 7Hz. Sampling is differential, binary, and non-adaptive, and includes information on multiple partitioning of the image which later allows us to determine the actual field of view. Reconstruction is based on the differential Fourier domain regularized inversion (D-FDRI). The proposed SPI framework is an alternative to both adaptive SPI, which is challenging to implement in real time, and to classical compressive sensing image recovery methods, which are very slow at high resolutions.

Single pixel imaging based on large capacity spatial multiplexing metasurface

Single pixel imaging as an alternative to traditional imaging methods, has attracted extensive attention in various research fields. Metasurfaces with subwavelength unit cells and compact footprint can be used as a substitute for traditional optical elements. In this work, we propose a single pixel imaging scheme based on metasurface composed of photon sieves, where spatial modulation is realized through shifting. Spatial multiplexing capability is demonstrated by this shifting mode, which can obtain more patterns in limited space and greatly increase the mask capacity. Benefited from the simple structure and easy manufacture of photon sieves, large capacity metasurface can be manufactured. Meanwhile, metasurfaces can simplify the single pixel imaging system, leading to the system miniaturization and integration. In addition, numerical and optical experiments prove that our proposal can operate at the range from the entire visible light to near-infrared light. Such scheme provides a new way for single pixel imaging and would be applied in microscopic imaging, dynamic imaging, hyperspectral imaging, and so on.

Object localization and tracking in three dimensions by space-to-time encoding

In this paper, we present a novel method for measuring the location and estimating the dynamics of fast-moving small objects in free space. The proposed 3D localization method is realized by a space-to-time optical transform and measurement of time-of-flight. We present the underlying physical and mathematical model of the method and provide an example based on a simple configuration. In the simplest mode, the method is implemented by two plane mirrors, a spherical light pulse illuminator, and a single fast response photodetector. The 3D spatial information is retrieved from the temporal measurements by solving an inverse problem that uses a sparse approximation of the scene. System simulation shows the ability to track fast small objects that are moving in space using only a single time-resolved detector.

Ghost panorama using a convex mirror

Computational ghost imaging or single-pixel imaging enables the image formation of an unknown scene using a lens-free photodetector. In this Letter, we present a computational panoramic ghost imaging system that can achieve a full-color panorama using a single-pixel photodetector, where a convex mirror performs the optical transformation of the engineered Hadamard-based circular illumination pattern from unidirectionally to omnidirectionally. To our best knowledge, it is the first time to propose the concept of ghost panoramas and realize preliminary experimentations. It is foreseeable that ghost panoramas will have more advantages in imaging and detection in many extreme conditions (e.g., scattering/turbulence and unconventional spectra), as well as broad application prospects.

Single-pixel camera based on a spinning mask

Single-pixel imaging (SPI) has been intensively studied in recent years for its capacity to obtain 2D images using a non-pixelated detector. However, the traditional modulation modality using an iteratively refreshed spatial light modulator has significantly restricted its imaging speed, which is a primary barrier to its widespread application. In this work, we propose and demonstrate a new, to the best of our knowledge, SPI scheme using a spinning mask for modulation. An annular binary mask is designed and spun to perform fast spatial modulation, neglecting the iterative modulation modality that limits SPI speed. A multi-spectral SPI system at 100 frames per second is demonstrated, covering a wide range of spectra, from ultraviolet to short-wave infrared light. We believe that this elegant and low-cost scheme will enable SPI to pave its way for practical application.