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Deep Learning and Artificial Intelligence in Signal Processing, Sensing and Biomedical Imaging

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

Deadline for manuscript submissions: 20 March 2026 | Viewed by 3143

Special Issue Editors

Prof. Dr. Peter Hockicko

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Information Technology, Department of Physics, University of Zilina, 010 26 Zilina, Slovakia
Interests: acoustic attenuation measurements; ion-conductive glasses; relaxation processes
Special Issues, Collections and Topics in MDPI journals
Dr. Patrik Kamencay

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Information Technology, Department of Multimedia and Information-Communication Technologies, University of Zilina, 010 26 Zilina, Slovakia
Interests: image segmentation; image analysis; feature extraction; computer vision; pattern recognition; digital image processing; object recognition; classification algorithms; image processing; neural network; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Robert Hudec

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Information Technology, Department of Multimedia and Information-Communication Technologies, University of Zilina, 010 26 Zilina, Slovakia
Interests: neural network; machine learning; deep learning; computer vision; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancements in deep learning (DL) and artificial intelligence (AI) have profoundly impacted various fields, with signal processing, sensing, and biomedical imaging being at the forefront. These technologies enable the extraction of meaningful information from complex data, improve diagnostic accuracy, enhance imaging techniques, and optimize sensing systems. This Special Issue aims to explore the latest research and developments in the application of DL and AI in these critical areas, showcasing innovative methodologies, groundbreaking results, and practical implementations. 

This Special Issue invites original research articles, reviews, and case studies that address topics including, but not limited to, the following:

  • Signal Processing:
  • AI and DL techniques for signal enhancement, and feature extraction.
  • Applications of AI in image processing and signal processing.
  • Novel algorithms for real-time image and signal processing.
  • Sensing:
  • AI-driven sensor data fusion and interpretation.
  • Smart sensors and IoT applications enhanced by AI.
  • AI techniques for environmental sensing and monitoring.
  • Biomedical Imaging:
  • AI and DL for image reconstruction, segmentation, and classification in medical imaging (MRI, CT, ultrasound, and other imaging modalities).
  • Application of DL algorithms for PPG signal processing.
  • AI-based diagnostic tools and decision support systems. 

This Special Issue on "Deep Learning and Artificial Intelligence in Signal Processing, Sensing, and Biomedical Imaging" aims to gather high-quality research that demonstrates the transformative impact of AI technologies in these domains.

Prof. Dr. Peter Hockicko
Dr. Patrik Kamencay
Dr. Robert Hudec
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

  • sensor technologies
  • sensing technologies
  • biomedical imaging
  • deep neural network
  • deep learning
  • machine learning
  • pattern recognition

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

21 pages, 39236 KB  
Article
Adaptive Image Deblurring Convolutional Neural Network with Meta-Tuning
by Quoc-Thien Ho, Minh-Thien Duong, Seongsoo Lee and Min-Cheol Hong
Sensors 2025, 25(16), 5211; https://doi.org/10.3390/s25165211 - 21 Aug 2025
Cited by 1 | Viewed by 1167
Abstract
Motion blur is a complex phenomenon caused by the relative movement between an observed object and an imaging sensor during the exposure time, resulting in degradation in the image quality. Deep-learning-based methods, particularly convolutional neural networks (CNNs), have shown promise in motion deblurring. [...] Read more.
Motion blur is a complex phenomenon caused by the relative movement between an observed object and an imaging sensor during the exposure time, resulting in degradation in the image quality. Deep-learning-based methods, particularly convolutional neural networks (CNNs), have shown promise in motion deblurring. However, the small kernel sizes of CNNs limit their ability to achieve optimal performance. Moreover, supervised deep-learning-based deblurring methods often exhibit overfitting in their training datasets. Models trained on widely used synthetic blur datasets frequently fail to generalize in other blur domains in real-world scenarios and often produce undesired artifacts. To address these challenges, we propose the Spatial Feature Selection Network (SFSNet), which incorporates a Regional Feature Extractor (RFE) module to expand the receptive field and effectively select critical spatial features in order to improve the deblurring performance. In addition, we present the BlurMix dataset, which includes diverse blur types, as well as a meta-tuning strategy for effective blur domain adaptation. Our method enables the network to rapidly adapt to novel blur distributions with minimal additional training, and thereby improve generalization. The experimental results show that the meta-tuning variant of the SFSNet eliminates unwanted artifacts and significantly improves the deblurring performance across various blur domains. Full article
(This article belongs to the Special Issue Deep Learning and Artificial Intelligence in Signal Processing, Sensing and Biomedical Imaging)
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15 pages, 9386 KB  
Article
Exploring Near- and Far-Field Effects in Photoplethysmography Signals Across Different Source–Detector Distances
by Ángel Solé Morillo, Joan Lambert Cause, Kevin De Pauw, Bruno da Silva and Johan Stiens
Sensors 2025, 25(1), 99; https://doi.org/10.3390/s25010099 - 27 Dec 2024
Viewed by 1369
Abstract
Photoplethysmography is a widely used optical technique to extract physiological information non-invasively. Despite its large use and adoption, multiple factors influence the signal shape and quality, including the instrumentation used. This work analyzes the variability of the DC component of the PPG signal [...] Read more.
Photoplethysmography is a widely used optical technique to extract physiological information non-invasively. Despite its large use and adoption, multiple factors influence the signal shape and quality, including the instrumentation used. This work analyzes the variability of the DC component of the PPG signal at three source–detector distances (6 mm, 9 mm, and 12 mm) using green, red, and infrared light and four photodiodes per distance. The coefficient of variation (CV) is proposed as a new signal quality index (SQI) to evaluate signal variabilities. This study first characterizes the PPG system, which is then used to acquire PPG signals in the chest of 14 healthy participants. Results show a great DC variability at 6 mm, homogenizing at 9 and 12 mm. This suggests that PPG systems are also sensitive to the near- and far-field effects commonly reported and studied in optics, which can impact the accuracy of physiological parameters dependent on the DC component, such as oxygen saturation (SpO2). Full article
(This article belongs to the Special Issue Deep Learning and Artificial Intelligence in Signal Processing, Sensing and Biomedical Imaging)
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