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New Trends in Optical Imaging and Sensing Technologies

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Optical Sensors".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 1799

Special Issue Editor

Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Interests: ultrashort laser pulses; fiber optical sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Optical imaging and sensing technologies are experiencing significant advancements, driven by the need for higher resolution, greater sensitivity, and enhanced functionalities in various fields such as medical diagnostics, environmental monitoring, and industrial applications. Particularly, enabled by innovations in devices, materials, fabrication techniques, and computational methods, recent trends highlight the development of fast and high-resolution imaging systems, hyperspectral imaging, computational and adaptive optics, fiber optics sensors, and novel photonic materials, and the integration of artificial intelligence (AI) into optical systems to enhance the design, image processing and data interpretation. Additionally, the miniaturization of optical components is leading to more compact and portable devices, expanding the accessibility and usability of these technologies in remote and resource-limited settings.

Several highlighted regions for the Special Issue (SI) include the following:

  • Ultrafast and high-speed imaging: leveraging femtosecond lasers and advanced photodetectors to achieve unprecedented temporal resolution.
  • High-resolution and super-resolution microscopy: dramatically increasing the ability to observe fine details at the nanoscale, surpassing the diffraction limit of light, allowing scientists to observe features, such as cellular processes, in unprecedented detail.
  • Integration of AI with optical sensing technologies: AI-driven, machine learning (ML) algorithms enhance image reconstruction, noise reduction, and data interpretation, enabling more accurate and faster diagnostics. This is particularly transformative in medical imaging, where AI can assist in early disease detection and personalized treatment planning.
  • Development of novel sensor designs and materials: new sensors, such as fiber sensors, and novel materials, such as nanophotonic materials, have opened new possibilities in optical sensing. The designs and materials can uniquely manipulate light, leading to the creation of highly sensitive and selective sensors, particularly beneficial in detecting low concentrations of substances in environmental and biomedical contexts.
  • Miniaturization of optical devices: by making components into portable and wearable devices, optical sensing technologies can be more capable, accessible and versatile. This trend facilitates endoscopy, real-time monitoring, and diagnostics in diverse settings, from point-of-care health monitoring to environmental surveillance.

Overall, these trends in optical imaging and sensing technologies are pushing the boundaries of scientific research and providing practical solutions for real-world challenges, leading to improved outcomes in health, environment, and industry.

Dr. Zhe Guang
Guest Editor

Manuscript Submission Information

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Keywords

  • ultrafast imaging
  • artificial intelligence
  • high-resolution imaging
  • hyperspectral imaging
  • wearable sensors
  • fiber optical sensors
  • biomedical imaging
  • miniaturization

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Published Papers (2 papers)

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Research

17 pages, 8825 KiB  
Article
Multiple CR Spatiotemporal Compressive Imaging System
by Xiaowen Hao, Dingaoyu Zhao and Jun Ke
Sensors 2025, 25(5), 1334; https://doi.org/10.3390/s25051334 - 21 Feb 2025
Viewed by 336
Abstract
Higher spatial and temporal resolutions are two important performance parameters in an imaging system. However, due to hardware limitations, the two resolutions are usually mutually restricted. To meet this challenge, we propose a spatiotemporal compressive imaging (STCI) system to reconstruct high-spatiotemporal-resolution images from [...] Read more.
Higher spatial and temporal resolutions are two important performance parameters in an imaging system. However, due to hardware limitations, the two resolutions are usually mutually restricted. To meet this challenge, we propose a spatiotemporal compressive imaging (STCI) system to reconstruct high-spatiotemporal-resolution images from low-resolution measurements. For STCI, we also designed a novel reconstruction network for multiple compression ratio (CR). To verify the effectiveness of our method, we implemented simulation and optical experiments, respectively. The experiment results show that our method can effectively reconstruct high-spatiotemporal-resolution target scenes for nine different CRs. With the maximum spatiotemporal CR of 128:1, our method can achieve a reconstruction accuracy of 28.28 dB. Full article
(This article belongs to the Special Issue New Trends in Optical Imaging and Sensing Technologies)
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22 pages, 5386 KiB  
Article
A Novel Multi-Sensor Nonlinear Tightly-Coupled Framework for Composite Robot Localization and Mapping
by Lu Chen, Amir Hussain, Yu Liu, Jie Tan, Yang Li, Yuhao Yang, Haoyuan Ma, Shenbing Fu and Gun Li
Sensors 2024, 24(22), 7381; https://doi.org/10.3390/s24227381 - 19 Nov 2024
Cited by 1 | Viewed by 1047
Abstract
Composite robots often encounter difficulties due to changes in illumination, external disturbances, reflective surface effects, and cumulative errors. These challenges significantly hinder their capabilities in environmental perception and the accuracy and reliability of pose estimation. We propose a nonlinear optimization approach to overcome [...] Read more.
Composite robots often encounter difficulties due to changes in illumination, external disturbances, reflective surface effects, and cumulative errors. These challenges significantly hinder their capabilities in environmental perception and the accuracy and reliability of pose estimation. We propose a nonlinear optimization approach to overcome these issues to develop an integrated localization and navigation framework, IIVL-LM (IMU, Infrared, Vision, and LiDAR Fusion for Localization and Mapping). This framework achieves tightly coupled integration at the data level using inputs from an IMU (Inertial Measurement Unit), an infrared camera, an RGB (Red, Green and Blue) camera, and LiDAR. We propose a real-time luminance calculation model and verify its conversion accuracy. Additionally, we designed a fast approximation method for the nonlinear weighted fusion of features from infrared and RGB frames based on luminance values. Finally, we optimize the VIO (Visual-Inertial Odometry) module in the R3LIVE++ (Robust, Real-time, Radiance Reconstruction with LiDAR-Inertial-Visual state Estimation) framework based on the infrared camera’s capability to acquire depth information. In a controlled study, using a simulated indoor rescue scenario dataset, the IIVL-LM system demonstrated significant performance enhancements in challenging luminance conditions, particularly in low-light environments. Specifically, the average RMSE ATE (Root Mean Square Error of absolute trajectory Error) improved by 23% to 39%, with reductions from 0.006 to 0.013. At the same time, we conducted comparative experiments using the publicly available TUM-VI (Technical University of Munich Visual-Inertial Dataset) without the infrared image input. It was found that no leading results were achieved, which verifies the importance of infrared image fusion. By maintaining the active engagement of at least three sensors at all times, the IIVL-LM system significantly boosts its robustness in both unknown and expansive environments while ensuring high precision. This enhancement is particularly critical for applications in complex environments, such as indoor rescue operations. Full article
(This article belongs to the Special Issue New Trends in Optical Imaging and Sensing Technologies)
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