Ghost Imaging: From Quantum to Artificial Intelligence

Topic Information

Dear Colleagues,

Ghost imaging is an alternative to conventional image capture with digital cameras, which can achieve greater sensitivity and/or resolution than classical optics utilizing correlation measurement. Ghost imaging has been demonstrated both with quantum light and classical light sources. It has been quickly adapted to other fields, including time domain imaging, X-ray imaging, THz imaging, and neutron imaging. In addition, with the advances in artificial intelligence, ghost imaging through deep learning has recently shown significant improvements in speed, resolution, and robustness. Recent developments in methods and techniques have made it possible to move from proof-of-principle to applications, such as biomedical imaging and earth and space science. Our intention is to present a variety of ideas, results, and discussions about ghost imaging in this issue. We hope this could be helpful to further research and practical applications of ghost imaging itself and research and practical applications in other fields.

Dr. Tao Peng
Prof. Dr. Yanhua Shih
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Entropy entropy	2.1	4.9	1999	22.3 Days	CHF 2600
Instruments instruments	-	2.6	2017	28 Days	CHF 1400
Photonics photonics	2.1	2.6	2014	14.9 Days	CHF 2400
Quantum Beam Science qubs	1.3	2.8	2017	22 Days	CHF 1600
Quantum Reports quantumrep	-	3.3	2019	16.4 Days	CHF 1400
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600

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Published Papers (2 papers)

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All Entropy Instruments Photonics QuBS Quantum Reports Sensors

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10 pages, 2560 KiB  

Open AccessCommunication

Hadamard Single-Pixel Imaging Based on Positive Patterns

by Rui Sun, Jiale Long, Yi Ding, Jiaye Kuang and Jiangtao Xi

Photonics 2023, 10(4), 395; https://doi.org/10.3390/photonics10040395 - 2 Apr 2023

Cited by 9 | Viewed by 2228

Abstract

Hadamard single-pixel imaging (SPI) employs the differential measurement strategy to eliminate the effect of negative value of Hadamard basis patterns but leads to doubling the number of measurements. To reduce the number of measurements, a Hadamard SPI method based on positive patterns is [...] Read more.

Hadamard single-pixel imaging (SPI) employs the differential measurement strategy to eliminate the effect of negative value of Hadamard basis patterns but leads to doubling the number of measurements. To reduce the number of measurements, a Hadamard SPI method based on positive patterns is proposed. In this method, only the positive patterns are used to acquire measurement values and reconstruct images, so the number of measurements will be reduced by 1/2. Combined with the intensity correlation theory of ghost imaging, the average value of the acquired measures is found; this average value is subtracted from all the measurement values to obtain the spectral coefficients, thus the background noise is eliminated to ensure the imaging quality. Simulation and experimental results show that the proposed method has good noise robustness and can efficiently reconstruct high quality images. Full article

(This article belongs to the Topic Ghost Imaging: From Quantum to Artificial Intelligence)

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27 pages, 13085 KiB  

Open AccessArticle

Multi-Objective Location and Mapping Based on Deep Learning and Visual Slam

by Ying Sun, Jun Hu, Juntong Yun, Ying Liu, Dongxu Bai, Xin Liu, Guojun Zhao, Guozhang Jiang, Jianyi Kong and Baojia Chen

Sensors 2022, 22(19), 7576; https://doi.org/10.3390/s22197576 - 6 Oct 2022

Cited by 20 | Viewed by 3727

Abstract

Simultaneous localization and mapping (SLAM) technology can be used to locate and build maps in unknown environments, but the constructed maps often suffer from poor readability and interactivity, and the primary and secondary information in the map cannot be accurately grasped. For intelligent [...] Read more.

Simultaneous localization and mapping (SLAM) technology can be used to locate and build maps in unknown environments, but the constructed maps often suffer from poor readability and interactivity, and the primary and secondary information in the map cannot be accurately grasped. For intelligent robots to interact in meaningful ways with their environment, they must understand both the geometric and semantic properties of the scene surrounding them. Our proposed method can not only reduce the absolute positional errors (APE) and improve the positioning performance of the system but also construct the object-oriented dense semantic point cloud map and output point cloud model of each object to reconstruct each object in the indoor scene. In fact, eight categories of objects are used for detection and semantic mapping using coco weights in our experiments, and most objects in the actual scene can be reconstructed in theory. Experiments show that the number of points in the point cloud is significantly reduced. The average positioning error of the eight categories of objects in Technical University of Munich (TUM) datasets is very small. The absolute positional error of the camera is also reduced with the introduction of semantic constraints, and the positioning performance of the system is improved. At the same time, our algorithm can segment the point cloud model of objects in the environment with high accuracy. Full article

(This article belongs to the Topic Ghost Imaging: From Quantum to Artificial Intelligence)

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