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

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.