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

Improving efficiency of ghost imaging by tapping into the obtained data

In ghost imaging, the imaging quality can be improved to a limit by increasing the number of samplings theoretically. However, the maximum achievable image quality is typically much lower than the theoretical limit. This maximum quality value, as well as the number of required samplings, can be regarded as representations of the capability to acquire information for an imaging system. In this paper, we propose the definition of imaging efficiency to quantitatively evaluate the capability of information acquisition. Based on the observation that the detected information is not fully reflected into the reconstructed image, we suggest to improve imaging efficiency by tapping into the obtained data. As an example, we demonstrate a post-processing method to improve imaging efficiency. The results showed that our method not only greatly improved the max value of imaging quality, but also reduced the number of required samplings needed to approach this max value.

A high-resolution field of view expansion method for single-pixel imaging systems based on DMD and scanning mirror

Single-pixel imaging (SPI) has significant advantages in detecting complex environments such as solid noise, high scattering, and long distances. In response to the contradiction between the field of view and resolution in traditional SPI systems, we propose a field of view expansion imaging method based on a digital micromirror device (DMD) and scanning mirror. Combining the advantages of traditional point scanning and SPI, we use high-precision control of the scanning mirror (with an error control of ± 3 mV) to scan and expand the reflected image on the target object onto the DMD, then using compressed sensing to achieve efficient sampling, thereby expanding the imaging field of view. We use the optimized TVAL3 algorithm for image recovery and reconstruction and stitching is performed. For second and third order imaging, results show that the imaging resolution of the individual and continuous targets have been improved by about 4 times and 9 times, the peak signal-to-noise ratio (PSNR) has been increased by about 2 times and 3 times, respectively. The experimental results indicate that the proposed imaging method may gradually adapt to existing beam scanning imaging systems. It will be beneficial for the widespread application of high-resolution imaging in large fields of view.

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.

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.

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 through non-homogeneous turbid media with adaptive illumination

The presence of scattering media limits the quality of images obtained by optical systems. Single-pixel imaging techniques based on structured illumination are highly tolerant to the presence of scattering between the object and the sensor, but very sensitive when the scattering medium is between the light source and the object. This makes it difficult to develop single-pixel imaging techniques for the case of objects immersed in scattering media. We present what we believe to be a new system for imaging objects through inhomogeneous scattering media in an epi-illumination configuration. It works in an adaptive way by combining diffuse optical imaging (DOI) and single pixel imaging (SPI) techniques in two stages. First, the turbid media is characterized by projecting light patterns with an LED array and applying DOI techniques. Second, the LED array is programmed to project light only through the less scattering areas of the media, while simultaneously using a digital micromirror device (DMD) to project light patterns onto the target using Hadamard basis coding functions. With this adaptive technique, we are able to obtain images of targets through two different scattering media with better quality than using conventional illumination. We also show that the system works with fluorescent targets.

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.

Photon-level single-pixel 3D tomography with masked attention network

Tomography plays an important role in characterizing the three-dimensional structure of samples within specialized scenarios. In the paper, a masked attention network is presented to eliminate interference from different layers of the sample, substantially enhancing the resolution for photon-level single-pixel tomographic imaging. The simulation and experimental results have demonstrated that the axial resolution and lateral resolution of the imaging system can be improved by about 3 and 2 times respectively, with a sampling rate of 3.0 %. The scheme is expected to be seamlessly integrated into various tomography systems, which is conducive to promoting the tomographic imaging for biology, medicine, and materials science.

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.

Feature ghost imaging for color identification

On the basis of computational ghost imaging (CGI), we present a new imaging technique, feature ghost imaging (FGI), which can convert the color information into distinguishable edge features in retrieved grayscale images. With the edge features extracted by different order operators, FGI can obtain the shape and the color information of objects simultaneously in a single-round detection using one single-pixel detector. The feature distinction of rainbow colors is presented in numerical simulations and the verification of FGI's practical performance is conducted in experiments. Furnishing a new perspective to the imaging of colored objects, our FGI extends the function and the application fields of traditional CGI while sustaining the simplicity of the experimental setup.