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Imaging and Sensing in Fiber Optics and Photonics: 2nd Edition

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

Deadline for manuscript submissions: 10 July 2025 | Viewed by 543

Special Issue Editors


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Guest Editor
Department of Electrical Engineering, Feng Chia University, Taichung 407, Taiwan
Interests: optical design; thin film residual stress; optical metrology; fiber-optic sensors; LMR sensors; thin-film measurements; mcro-optics devices
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Electrical Engineering, Chinese Culture University, Taipei 111396, Taiwan
Interests: information optics; photonics; digital holography; 3D surface imaging; intelligent and precision measurement

Special Issue Information

Dear Colleagues,

Optics and photonics technologies are particularly suitable for the development of high-sensitivity sensors. Furthermore, imaging and sensing techniques could pave the way for many important biomedical applications. Due to the wide application of optical sensors, the design of innovative optical sensing techniques for specific applications requires an in-depth understanding of the optical, material, and environmental properties that affect the performance of optical and photonic sensors. To date, many innovative structures, along with novel materials and different preparation techniques, have emerged in the design and preparation of optical and photonic sensors.

This Special Issue welcomes the submission of original and review articles by leading groups and scientists in the field of optical and photonic sensing technologies. Novel imaging and sensing techniques, as well as the results of their application, will be presented in this Special Issue.

Prof. Dr. Chuen-Lin Tien
Prof. Dr. Han-Yen Tu
Guest Editors

Manuscript Submission Information

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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

  • optical sensor
  • fiber-optic sensor
  • image sensor
  • biological and chemical sensors
  • thin film sensor
  • biomedical sensing
  • flexible sensor
  • imaging and sensing devices

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Published Papers (1 paper)

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Research

19 pages, 3691 KiB  
Article
ATDMNet: Multi-Head Agent Attention and Top-k Dynamic Mask for Camouflaged Object Detection
by Rui Fu, Yuehui Li, Chih-Cheng Chen, Yile Duan, Pengjian Yao and Kaixin Zhou
Sensors 2025, 25(10), 3001; https://doi.org/10.3390/s25103001 - 9 May 2025
Viewed by 279
Abstract
Camouflaged object detection (COD) encounters substantial difficulties owing to the visual resemblance between targets and their environments, together with discrepancies in multiscale representation of features. Current methodologies confront obstacles with feature distraction, modeling far-reaching dependencies, fusing multiple-scale details, and extracting boundary specifics. Consequently, [...] Read more.
Camouflaged object detection (COD) encounters substantial difficulties owing to the visual resemblance between targets and their environments, together with discrepancies in multiscale representation of features. Current methodologies confront obstacles with feature distraction, modeling far-reaching dependencies, fusing multiple-scale details, and extracting boundary specifics. Consequently, we propose ATDMNet, an amalgamated architecture combining CNN and transformer within a numerous phases feature extraction framework. ATDMNet employs Res2Net as the foundational encoder and incorporates two essential components: multi-head agent attention (MHA) and top-k dynamic mask (TDM). MHA improves local feature sensitivity and long-range dependency modeling by incorporating agent nodes and positional biases, whereas TDM boosts attention with top-k operations and multiscale dynamic methods. The decoding phase utilizes bilinear upsampling and sophisticated semantic guidance to enhance low-level characteristics, hence ensuring precise segmentation. Enhanced performance is achieved by deep supervision and a hybrid loss function. Experiments applying COD datasets (NC4K, COD10K, CAMO) demonstrate that ATDMNet establishes a new benchmark in both precision and efficiency. Full article
(This article belongs to the Special Issue Imaging and Sensing in Fiber Optics and Photonics: 2nd Edition)
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