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Infrared Imaging and Sensing Technology
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Infrared Imaging and Sensing Technology

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

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 8034

Special Issue Editors

Dr. Jun Huang

E-Mail Website
Guest Editor
Electronic Information School, Wuhan University, Wuhan 430072, China
Interests: infrared imaging; artificial intelligence; image processing; embedded system
Special Issues, Collections and Topics in MDPI journals
Dr. Fan Fan

E-Mail Website
Guest Editor
Electronic Information School, Wuhan University, Wuhan 430072, China
Interests: imaging processing; infrared and hyperspectral imaging; small target detection
Special Issues, Collections and Topics in MDPI journals
Dr. Hao Li

E-Mail Website
Guest Editor
School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430048, China
Interests: infrared and spectral signal processing technologies and theories
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Infrared imaging and sensing technology has been commonly utilized in a variety of applications, such as surveillance, manufacturing, medical imaging, and the military. However, there are still many challenges in this field, especially in the preprocessing of infrared images and specific algorithms for further applications.

This Special Issue of Sensors is devoted to discussing and reporting on novel progress in infrared imaging and sensing technology, especially related to improving infrared image quality and meeting the requirements of various applications. Paper proposals describing the enhancement, fusion, super resolution, nonuniform correction, etc. of infrared images are welcome. New procedures, algorithms, and solutions for detection, recognition, and medical diagnosis using infrared images are also encouraged.

Dr. Jun Huang
Dr. Fan Fan
Dr. Hao Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • infrared imaging
  • infrared sensing
  • infrared image enhancement
  • nonuniform correction
  • infrared image fusion
  • infrared image super resolution
  • infrared imaging application (include but not limited to detection, recognition, medical diagnosis)

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Published Papers (3 papers)

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Research

27 pages, 12917 KiB  
Article
LWIR Lateral Effect Position Sensitive HgCdTe Photodetector at 205 K
by Jarosław Pawluczyk, Mateusz Żbik and Józef Piotrowski
Sensors 2023, 23(10), 4915; https://doi.org/10.3390/s23104915 - 19 May 2023
Viewed by 2250
Abstract
We describe in detail the construction and characterization of a Peltier-cooled long-wavelength infrared (LWIR) position-sensitive detector (PSD) based on the lateral effect. The device was recently reported for the first time to the authors’ knowledge. It is a modified PIN HgCdTe photodiode, forming [...] Read more.
We describe in detail the construction and characterization of a Peltier-cooled long-wavelength infrared (LWIR) position-sensitive detector (PSD) based on the lateral effect. The device was recently reported for the first time to the authors’ knowledge. It is a modified PIN HgCdTe photodiode, forming the tetra-lateral PSD, with a photosensitive area of 1 × 1 mm2, operating at 205 K in the 3–11 µm spectral range, capable of achieving a position resolution of 0.3–0.6 µm using 10.5 µm 2.6 mW radiation focused on a spot of the 1/e2 diameter 240 µm, with a box-car integration time of 1 µs and correlated double sampling. Full article
(This article belongs to the Special Issue Infrared Imaging and Sensing Technology)
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14 pages, 6755 KiB  
Article
A Method for Detection of Corn Kernel Mildew Based on Co-Clustering Algorithm with Hyperspectral Image Technology
by Zhen Kang, Tianchen Huang, Shan Zeng, Hao Li, Lei Dong and Chaofan Zhang
Sensors 2022, 22(14), 5333; https://doi.org/10.3390/s22145333 - 17 Jul 2022
Cited by 13 | Viewed by 2299
Abstract
Hyperspectral imaging can simultaneously acquire spectral and spatial information of the samples and is, therefore, widely applied in the non-destructive detection of grain quality. Supervised learning is the mainstream method of hyperspectral imaging for pixel-level detection of mildew in corn kernels, which requires [...] Read more.
Hyperspectral imaging can simultaneously acquire spectral and spatial information of the samples and is, therefore, widely applied in the non-destructive detection of grain quality. Supervised learning is the mainstream method of hyperspectral imaging for pixel-level detection of mildew in corn kernels, which requires a large number of training samples to establish the prediction or classification models. This paper presents an unsupervised redundant co-clustering algorithm (FCM-SC) based on multi-center fuzzy c-means (FCM) clustering and spectral clustering (SC), which can effectively detect non-uniformly distributed mildew in corn kernels. This algorithm first carries out fuzzy c-means clustering of sample features, extracts redundant cluster centers, merges the cluster centers by spectral clustering, and finally finds the category of corresponding cluster centers for each sample. It effectively solves the problems of the poor ability of the traditional fuzzy c-means clustering algorithm to classify the data with complex structure distribution and the complex calculation of the traditional spectral clustering algorithm. The experimental results demonstrated that the proposed algorithm could describe the complex structure of mildew distribution in corn kernels and exhibits higher stability, better anti-interference ability, generalization ability, and accuracy than the supervised classification model. Full article
(This article belongs to the Special Issue Infrared Imaging and Sensing Technology)
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21 pages, 14795 KiB  
Article
Low-Altitude Infrared Slow-Moving Small Target Detection via Spatial-Temporal Features Measure
by Jing Mu, Junmin Rao, Ruimin Chen and Fanming Li
Sensors 2022, 22(14), 5136; https://doi.org/10.3390/s22145136 - 8 Jul 2022
Cited by 8 | Viewed by 2742
Abstract
Robust detection of infrared slow-moving small targets is crucial in infrared search and tracking (IRST) applications such as infrared guidance and low-altitude security; however, existing methods easily cause missed detection and false alarms when detecting infrared small targets in complex low-altitude scenes. In [...] Read more.
Robust detection of infrared slow-moving small targets is crucial in infrared search and tracking (IRST) applications such as infrared guidance and low-altitude security; however, existing methods easily cause missed detection and false alarms when detecting infrared small targets in complex low-altitude scenes. In this article, a new low-altitude slow-moving small target detection algorithm based on spatial-temporal features measure (STFM) is proposed. First, we construct a circular kernel to calculate the local grayscale difference (LGD) in a single image, which is essential to suppress low-frequency background and irregular edges in the spatial domain. Then, a short-term energy aggregation (SEA) mechanism with the accumulation of the moving target energy in multiple successive frames is proposed to enhance the dim target. Next, the spatial-temporal saliency map (STSM) is obtained by integrating the two above operations, and the candidate targets are segmented using an adaptive threshold mechanism from STSM. Finally, a long-term trajectory continuity (LTC) measurement is designed to confirm the real target and further eliminate false alarms. The SEA and LTC modules exploit the local inconsistency and the trajectory continuity of the moving small target in the temporal domain, respectively. Experimental results on six infrared image sequences containing different low-altitude scenes demonstrate the effectiveness of the proposed method, which performs better than the existing state-of-the-art methods. Full article
(This article belongs to the Special Issue Infrared Imaging and Sensing Technology)
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