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Applied Sciences
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Single-Pixel Intelligent Imaging and Recognition
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Single-Pixel Intelligent Imaging and Recognition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 1295

Special Issue Editor

Prof. Lu Gao

E-Mail Website
Guest Editor
School of Science, China University of Geosciences, Beijing 100083, China
Interests: ghost imaging; single-pixel imaging; identification of moving object

Special Issue Information

Dear Colleagues, 

This Special Issue focuses on the development and application of single-pixel imaging and ghost imaging technology. Single-pixel imaging, a type of computational imaging, originated from ghost imaging technology and achieves imaging through structured light illumination and the collection of light intensity information using a single-pixel detector. Ghost imaging underwent a development process from quantum ghost imaging to classical ghost imaging and then to computational ghost imaging. Combining deep learning algorithms for single-pixel imaging and moving object recognition has significant application advantages, especially in special bands such as infrared and terahertz bands. We welcome manuscripts involving theoretical and experimental research in related fields.

Prof. Lu Gao
Guest Editor

Manuscript Submission Information

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Keywords

  • ghost imaging
  • single-pixel imaging
  • recognition of moving object
  • deep learning
  • computational imaging

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Published Papers (2 papers)

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Research

11 pages, 21181 KB  
Article
Parallel Ghost Imaging with Extra Large Field of View and High Pixel Resolution
by Nixi Zhao, Changzhe Zhao, Jie Tang, Jianwen Wu, Danyang Liu, Han Guo, Haipeng Zhang and Tiqiao Xiao
Appl. Sci. 2025, 15(15), 8137; https://doi.org/10.3390/app15158137 - 22 Jul 2025
Cited by 1 | Viewed by 322
Abstract
Ghost imaging (GI) facilitates image acquisition under low-light conditions through single pixel measurements, thus holding tremendous potential across various fields such as biomedical imaging, remote sensing, defense and military applications, and 3D imaging. However, in order to reconstruct high-resolution images, GI typically requires [...] Read more.
Ghost imaging (GI) facilitates image acquisition under low-light conditions through single pixel measurements, thus holding tremendous potential across various fields such as biomedical imaging, remote sensing, defense and military applications, and 3D imaging. However, in order to reconstruct high-resolution images, GI typically requires a large number of single-pixel measurements, which imposes practical limitations on its application. Parallel ghost imaging addresses this issue by utilizing each pixel of a position-sensitive detector as a bucket detector to simultaneously perform tens of thousands of ghost imaging measurements in parallel. In this work, we explore the non-local characteristics of ghost imaging in depth, and by constructing a large speckle space, we achieve a reconstruction result in parallel ghost imaging where the field of view surpasses the limitations of the reference arm detector. Using a computational ghost imaging framework, after pre-recording the speckle patterns, we are able to complete X-ray ghost imaging at a speed of 6 min per sample, with image dimensions of 14,000 × 10,000 pixels (4.55 mm × 3.25 mm, millimeter-scale field of view) and a pixel resolution of 0.325 µm (sub-micron pixel resolution). We present this framework to enhance efficiency, extend resolution, and dramatically expand the field of view, with the aim of providing a solution for the practical implementation of ghost imaging. Full article
(This article belongs to the Special Issue Single-Pixel Intelligent Imaging and Recognition)
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24 pages, 2032 KB  
Article
ViT-Based Classification and Self-Supervised 3D Human Mesh Generation from NIR Single-Pixel Imaging
by Carlos Osorio Quero, Daniel Durini and Jose Martinez-Carranza
Appl. Sci. 2025, 15(11), 6138; https://doi.org/10.3390/app15116138 - 29 May 2025
Viewed by 700
Abstract
Accurately estimating 3D human pose and body shape from a single monocular image remains challenging, especially under poor lighting or occlusions. Traditional RGB-based methods struggle in such conditions, whereas single-pixel imaging (SPI) in the Near-Infrared (NIR) spectrum offers a robust alternative. NIR penetrates [...] Read more.
Accurately estimating 3D human pose and body shape from a single monocular image remains challenging, especially under poor lighting or occlusions. Traditional RGB-based methods struggle in such conditions, whereas single-pixel imaging (SPI) in the Near-Infrared (NIR) spectrum offers a robust alternative. NIR penetrates clothing and adapts to illumination changes, enhancing body shape and pose estimation. This work explores an SPI camera (850–1550 nm) with Time-of-Flight (TOF) technology for human detection in low-light conditions. SPI-derived point clouds are processed using a Vision Transformer (ViT) to align poses with a predefined SMPL-X model. A self-supervised PointNet++ network estimates global rotation, translation, body shape, and pose, enabling precise 3D human mesh reconstruction. Laboratory experiments simulating night-time conditions validate NIR-SPI’s potential for real-world applications, including human detection in rescue missions. Full article
(This article belongs to the Special Issue Single-Pixel Intelligent Imaging and Recognition)
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