Single-pixel camera based on a spinning mask

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.

Mobile-friendly under-sampling single-pixel imaging based on a lightweight hybrid CNN-ViT architecture

Single-pixel imaging (SPI) using deep learning networks, e.g., convolutional neural networks (CNNs) and vision transformers (ViTs), has made significant progress. However, these existing models, especially those based on ViT architectures, pose challenges due to their large number of parameters and computational loads, making them unsuitable for mobile SPI applications. To break through this limitation, we propose mobile ViT blocks to bring down the computation cost of traditional ViTs, and combine CNNs to design what we believe to be a novel lightweight CNN-ViT hybrid model for efficient and accurate SPI reconstruction. In addition, we also propose a general-purpose differential ternary modulation pattern scheme for deep learning SPI (DLSPI), which is training-friendly and hardware-friendly. Simulations and real experiments demonstrate that our method has higher imaging quality, lower memory consumption, and less computational burden than the state-of-the-art DLSPI methods.

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.

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.

Double filter iterative ghost imaging for high quality edge and image acquisition

Improving imaging quality and reducing time consumption are the key problems that need to be solved in the practical application of ghost imaging. Hence, we demonstrate a double filter iterative ghost imaging method, which adopts the joint iteration of projected Landweber iterative regularization and double filtering based on block matching three dimensional filtering and guided filtering to achieve high-quality image reconstruction under low measurement and low iteration times. This method combines the advantages of ill-posed problem solution of projected Landweber iterative regularization with double filtering joint iterative de-noising and edge preservation. The numerical simulation results show that our method outperforms the comparison method by 4 to 6 dB in terms of peak signal-to-noise ratio for complex binary target 'rice' and grayscale target 'aircraft' after 1500 measurements. The comparison results of experiments and numerical simulations using similar aircraft targets show that this method is superior to the comparison method, especially in terms of richer and more accurate edge detection results. This method can simultaneously obtain high quality reconstructed image and edge feature information under low measurement and iteration times, which is of great value for the practical application fields of imaging and edge detection at the same time, such as intelligent driving, remote sensing and other fields.

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.

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.

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.

A Single-Pixel Imaging Scheme with Obstacle Detection

Single-pixel imaging (SPI) utilizes a second-order correlation of structured illumination light field and a single-pixel detector to form images. As the single-pixel detector provides no spatial resolution, a structured illumination light field generated by devices such as a spatial light modulator substitutes the role of array camera to retrieve pixel-wise spatial information. Due to its unique imaging modality, SPI has certain advantages. Meanwhile, its counterintuitive configuration and reciprocity relation to traditional array cameras have been studied to understand its fundamental principle. According to previous studies, the non-spatial detection property makes it possible for SPI to resist scattering in the detection part. In this work, we study the influence of an obstacle aperture in the detection part of SPI. We notice that such an obstacle aperture can restrict the field-of-view (FOV) of SPI, which can be diminished by a scattering process. We investigate these properties with experiment results and analysis under geometry optics. We believe that our study will be helpful in understanding the counterintuitive configuration of SPI and its reciprocity to traditional imaging.