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Advanced Sensors for Image Processing and Analysis
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Advanced Sensors for Image Processing and Analysis

A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Sensing and Imaging".

Deadline for manuscript submissions: 27 November 2026 | Viewed by 2521

Editors

Dr. Yoichi Kageyama

E-Mail Website
Guest Editor
Faculty of Informatics and Data Science, Akita University, 1-1 Tegata Gakuen-machi, Akita-shi 010-8502, Akita, Japan
Interests: image processing; machine learning; pattern recognition
Prof. Dr. Lifeng Zhang

E-Mail Website
Guest Editor
Department of Electrical Engineering and Electronics, Faculty of Engineering, Kyushu Institute of Technology, Kitakyushu, Japan
Interests: image enhancement; image compression; object detection

Special Issue Information

Dear Colleagues,

Advances in AI and IoT technologies are rapidly accelerating the evolution of image processing. These technologies are now integrated into many aspects of our daily lives. However, sensor data are often susceptible to noise and distortion, making it challenging to extract useful features for subsequent processing. To overcome these limitations, it is crucial to utilize new data processing techniques and multiple features effectively, as well as to conduct an in-depth analysis based on the results.

This Special Issue invites submissions on advances in signal and image processing algorithms, techniques, and both theoretical and applied research for all types of sensors and sensing methods. We are particularly interested in research on image recognition applications that help us to understand our surroundings. In addition to experimental and theoretical research results, review articles are welcome.

Dr. Yoichi Kageyama
Prof. Dr. Lifeng Zhang
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • image processing
  • human centered sensing
  • remote sensing
  • medical and biomedical image analysis and understanding
  • thermography
  • infrared imaging
  • thermography
  • infrared imaging
  • machine learning
  • artificial intelligence (AI)
  • internet of things (IoT)
  • pattern recognition

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Published Papers (4 papers)

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Research

19 pages, 12926 KB  
Article
Dual-Branch Multi-Perspective Modulation Network for Efficient Infrared Image Super-Resolution
by Zepeng Liu, Duanyang Zhang, Ruimin Qi, Guodong Zhang, Yizong Wang and Lizhi Liu
Sensors 2026, 26(15), 4928; https://doi.org/10.3390/s26154928 - 4 Aug 2026
Viewed by 373
Abstract
Transformer-based super-resolution methods achieve competitive performance by capturing non-local information through self-attention. Nevertheless, the computation of the self-attention introduces heavy computational overhead, and its inherent low-pass characteristic restricts the learning of local details. To address these problems, we propose an effective dual-branch multi-perspective [...] Read more.
Transformer-based super-resolution methods achieve competitive performance by capturing non-local information through self-attention. Nevertheless, the computation of the self-attention introduces heavy computational overhead, and its inherent low-pass characteristic restricts the learning of local details. To address these problems, we propose an effective dual-branch multi-perspective modulation network (DMMN) for efficient infrared image super-resolution. Specifically, we design a multi-scale feature modulation enhancement unit (MFMEU) to capture cross-scale spatial features and a frequency-domain cross-correlation patch modulation unit (FCPMU) to explore global feature representations. We further develop an efficient bidirectional cross modulation unit (BCMU) to promote feature interaction between outputs of MFMEU and FCPMU. Extensive experimental results verify that DMMN achieves a competitive trade-off between reconstruction accuracy and computational efficiency. For instance, compared with the ×4 SRFormer-light, the proposed DMMN obtains an average gain of 0.08 dB in PSNR across five public test datasets, runs 2.7× faster, and uses only about 24% of the FLOPs. Full article
(This article belongs to the Special Issue Advanced Sensors for Image Processing and Analysis)
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21 pages, 5052 KB  
Article
Subpixel Edge Localization via Ruled-Sigmoid Surfaces and Its Application for Precision Analysis of Cycloidal Gear Profiles
by Jing Zhang, Po Du, Wenzhen Zhao and Wenhui Zhao
Sensors 2026, 26(13), 4193; https://doi.org/10.3390/s26134193 - 2 Jul 2026
Viewed by 414
Abstract
To overcome the limitations of single-dimensional data and low efficiency in traditional cycloidal gear inspection, a comprehensive machine vision-based method was proposed. A high-precision vision platform was established, and a Sigmoid surface-based edge detection algorithm was employed for sub-pixel edge localization. Logarithmic transformation [...] Read more.
To overcome the limitations of single-dimensional data and low efficiency in traditional cycloidal gear inspection, a comprehensive machine vision-based method was proposed. A high-precision vision platform was established, and a Sigmoid surface-based edge detection algorithm was employed for sub-pixel edge localization. Logarithmic transformation combined with light intensity compensation was applied to correct saturation-induced errors. The pixel equivalent and compensation coefficient were systematically calibrated using a dot-matrix plate and gauge blocks. A sub-pixel tooth profile model in the physical coordinate system was reconstructed through pixel equivalent calibration, dynamic light intensity compensation, and multi-coordinate transformation. Comparative tests against a coordinate measuring machine (CMM) verified that the point-to-point deviation between the two measurement systems was within 10 μm (maximum 11.62 μm). The inherent tooth profile deviation of the tested cycloidal gears, which reflects the machining quality of workpieces, ranged from 24 μm to 37 μm. Multiple repeated tests prove that the system achieves a repeat positioning accuracy of 0.8 μm. Based on the measurement characteristics, a hybrid analytical method integrating Cartesian and polar coordinate systems was developed, enabling the simultaneous evaluation of critical geometric tolerances, such as the diameters of the center hole and crankshaft hole. The full inspection cycle for cycloidal gears was reduced to 13 s, which demonstrates a substantial efficiency improvement over traditional methods. Full article
(This article belongs to the Special Issue Advanced Sensors for Image Processing and Analysis)
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27 pages, 36204 KB  
Article
Full-Field 3D Displacement Measurement of Suspended Ceiling Systems Under Seismic Loading Using a Consumer-Grade Multi-Camera Framework
by Mearge Kahsay Seyfu, Yuan-Sen Yang, Cameron C. W. Flude, David T. Lau, Jeffrey Erochko and Hung-Wei Liu
Sensors 2026, 26(13), 4011; https://doi.org/10.3390/s26134011 - 24 Jun 2026
Viewed by 519
Abstract
Suspended ceiling systems are among the most seismically vulnerable non-structural components in buildings, posing significant life-safety risks and economic losses, yet understanding their full-field kinematic behavior under seismic loading remains a major experimental challenge. Conventional contact sensors offer limited spatial coverage and can [...] Read more.
Suspended ceiling systems are among the most seismically vulnerable non-structural components in buildings, posing significant life-safety risks and economic losses, yet understanding their full-field kinematic behavior under seismic loading remains a major experimental challenge. Conventional contact sensors offer limited spatial coverage and can alter the dynamic properties of lightweight panels due to mass loading. In contrast, non-contact optical alternatives are rarely feasible in shake-table environments due to restricted viewing angles, extensive areal coverage requirements, and the risk of equipment damage from falling panels. This study proposes an end-to-end three-dimensional displacement measurement framework for large-scale shake-table testing of suspended ceiling systems, employing consumer-grade cameras with purpose-built tools that cover the complete experimental workflow, including motion-based video trimming, semi-automated calibration, a robust multi-stage image-tracking pipeline that maintains trajectory continuity under extreme inter-frame displacements, and a ceiling system motion visualization and analysis tool. The framework was validated through a full-scale shake-table experiment continuously tracking 324 spatial nodes across 81 ceiling panels, achieving an RMSE below 3 mm in all spatial directions and exact peak-frequency agreement in 9 out of 10 test cases. A parallel processing architecture reduced total processing time from over 27 h to under 10 min without GPU acceleration, and six-degree-of-freedom rigid-body analysis resolved the complete panel failure sequence from constrained oscillation through multi-axis rotation to gravitational free fall, a level of kinematic detail unattainable with conventional instrumentation. This framework establishes a practical, scalable foundation for full-field seismic performance assessment of non-structural systems where conventional instrumentation is physically or logistically infeasible. Full article
(This article belongs to the Special Issue Advanced Sensors for Image Processing and Analysis)
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29 pages, 7442 KB  
Article
Image Similarity Judgment Method for Waste Printed Circuit Boards
by Hikaru Shirai, Ryo Oishi, Yoichi Kageyama, Kazune Sasaki, Keita Ogawa and Satoshi Nakagawara
Sensors 2026, 26(4), 1224; https://doi.org/10.3390/s26041224 - 13 Feb 2026
Viewed by 649
Abstract
Waste printed circuit boards (WPCBs) contain valuable metals such as gold, palladium, and silver, which are typically recovered through non-ferrous metal smelting. Currently, WPCBs are manually classified by workers, who visually compare board colors and component layouts with previously processed boards. This approach [...] Read more.
Waste printed circuit boards (WPCBs) contain valuable metals such as gold, palladium, and silver, which are typically recovered through non-ferrous metal smelting. Currently, WPCBs are manually classified by workers, who visually compare board colors and component layouts with previously processed boards. This approach is time-consuming and prone to human error. To address these limitations, we propose an image-based algorithm for automated WPCB similarity assessment. The method extracts visual features from board images and computes similarity scores, incorporating classification strategies based on board-specific characteristics. Key features identified as effective for similarity evaluation include the hue value, coefficient of variation in terminal regions, number of line elements in terminal regions, structural complexity, and number of integrated circuits. Weighted feature contributions further improve accuracy. Our experimental results demonstrate that the proposed approach achieves 88.0% accuracy for the targeted PCB types, outperforming a comparative self-supervised contrastive learning method. This image-driven solution can significantly streamline WPCB recycling by reducing reliance on manual inspection and improving operational efficiency. Full article
(This article belongs to the Special Issue Advanced Sensors for Image Processing and Analysis)
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