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Electronics
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Deep Learning in Video and Image Processing: Challenges, Solutions, and...
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Deep Learning in Video and Image Processing: Challenges, Solutions, and Future Directions, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 August 2026 | Viewed by 2467

Special Issue Editors

Dr. Abdussalam Elhanashi

E-Mail Website
Guest Editor
Department of Information Engineering, University of Pisa, Via Girolamo Caruso, 16, 56122 Pisa, Italy
Interests: deep learning; machine Learning; video processing; image processing; Internet of Things, cybersecurity; embedded systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sergio Saponara

E-Mail Website
Guest Editor
Department of Information Engineering, University of Pisa, Via Girolamo Caruso, 16, 56122 Pisa, Italy
Interests: automotive electronics; embedded HPC (high-performance computing); enabling technologies IoT (Internet of Things)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue “Deep Learning in Video and Image Processing: Challenges, Solutions, and Future Directions, 2nd Edition” focuses on advancing the integration of deep learning and machine learning (ML) techniques with video and image processing directly on edge devices. This collection aims to address the unique challenges of executing computationally intensive DL algorithms in real time on resource-constrained devices, such as those with limited processing power, memory, and energy consumption. The purpose is to explore innovative solutions that enhance the efficiency, accuracy, and reliability of ML applications in real-world scenarios. The scope covers a broad spectrum of topics, including but not limited to algorithm optimization, hardware-software co-design, energy-efficient ML models, and real-time data processing techniques. This Special Issue will significantly contribute to the existing literature by bridging the gap between theoretical DL advancements and practical edge computing implementations. While current research predominantly focuses on cloud-based solutions or offline processing, this Special Issue emphasizes the need for immediate, localized processing, which is crucial for latency-sensitive applications. Examples of real-world applications include surveillance systems that require instant anomaly detection, medical imaging for real-time diagnostics, autonomous vehicles needing immediate object recognition and decision-making, smart cameras in urban traffic management, augmented reality devices for interactive user experiences, industrial automation for monitoring and control, wildlife monitoring for real-time tracking, disaster response systems for rapid situational analysis, smart home devices for enhanced security and convenience, and wearable technology for health monitoring and personalized feedback. By presenting cutting-edge research and practical case studies, this Special Issue will serve as a valuable resource for researchers, engineers, and practitioners aiming to develop and deploy efficient DL solutions on edge platforms, ultimately advancing the field of real-time video and image processing.

Dr. Abdussalam Elhanashi
Prof. Dr. Sergio Saponara
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics 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 2400 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

  • real-time machine learning systems
  • edge computing and edge intelligence
  • edge AI and on-device learning
  • image and video processing
  • real-time visual and multimodal data analytics
  • computational intelligence and learning systems
  • generative AI and foundation models
  • multimodal learning and data fusion
  • agentic AI and autonomous decision-making systems
  • algorithmic optimization and model compression
  • hardware–software co-design
  • energy-efficient and low-power machine learning
  • latency-critical and time-sensitive applications
  • computational, memory, and energy efficiency
  • resource-constrained and embedded devices
  • anomaly detection and event recognition
  • real-time diagnostics and intelligent monitoring
  • autonomous and intelligent transportation systems
  • object detection, recognition, and tracking
  • urban traffic analytics and smart mobility
  • augmented, virtual, and mixed reality systems
  • industrial automation and smart manufacturing
  • healthcare and medical AI applications
  • medical image and video analysis
  • wearable computing and health monitoring
  • streaming and real-time data processing
  • machine learning deployment, inference, and lifecycle management
  • privacy-preserving, secure, and trustworthy AI
  • smart vision sensors and intelligent camera systems
  • real-world, scalable, and production-ready AI systems
  • industrial computer vision and visual inspection
  • streaming and real-time industrial data processing
  • robotics and autonomous industrial systems

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Related Special Issue

Published Papers (3 papers)

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Research

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15 pages, 4018 KB  
Article
Combining Interpolation Techniques and Lightweight Convolutional Neural Networks for Partial Discharge Image Signal Identification in Transformer Bushings
by Yi-Pin Hsu
Electronics 2026, 15(8), 1584; https://doi.org/10.3390/electronics15081584 - 10 Apr 2026
Viewed by 303
Abstract
Partial discharge detection is a key technology for maintaining the normal operation of industrial power equipment. Oil-impregnated paper bushings are crucial components connecting transformers to the power grid. Insulation degradation leads to partial discharge, posing a significant threat to power system operation. Developing [...] Read more.
Partial discharge detection is a key technology for maintaining the normal operation of industrial power equipment. Oil-impregnated paper bushings are crucial components connecting transformers to the power grid. Insulation degradation leads to partial discharge, posing a significant threat to power system operation. Developing on-line diagnostics for partial discharge in transformer bushings and automatic identification of insulation defects can effectively protect system and personnel safety. Due to limitations of small sample sizes and lightweight networks, this study combines interpolation techniques with a lightweight convolutional neural network to improve identification accuracy. This network uses interpolation to maintain the undistorted sample signal from the initial input and reduces training defects from a small sample size. The neural network extracts partial discharge features to determine the defect type and its cause. This study uses a publicly available dataset with discharge signals from generators. Although from a different source from the discharge signals generated by oil-impregnated paper bushings, the signal distribution is similar, allowing for a fair analysis and providing a reference for evaluating discharge signals obtained from oil-impregnated paper bushings or other discharge devices. The experimental results show that the accuracy of this network improved from 97% to over 99% while maintaining low computational complexity and excellent real-time performance. Furthermore, this network was implemented and validated on existing industrial equipment. Full article
(This article belongs to the Special Issue Deep Learning in Video and Image Processing: Challenges, Solutions, and Future Directions, 2nd Edition)
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22 pages, 2506 KB  
Article
CycleGAN-Based Data Augmentation for Scanning Electron Microscope Images to Enhance Integrated Circuit Manufacturing Defect Classification
by Andrew Yen, Nemo Chang, Jean Chien, Lily Chuang and Eric Lee
Electronics 2026, 15(4), 803; https://doi.org/10.3390/electronics15040803 - 13 Feb 2026
Viewed by 490
Abstract
Semiconductor defect inspection is frequently hindered by data scarcity and the resulting class imbalance in supervised learning. This study proposes a CycleGAN-based data augmentation pipeline designed to synthesize realistic defective CD-SEM images from abundant normal patterns, incorporating a quantitative quality control mechanism. Using [...] Read more.
Semiconductor defect inspection is frequently hindered by data scarcity and the resulting class imbalance in supervised learning. This study proposes a CycleGAN-based data augmentation pipeline designed to synthesize realistic defective CD-SEM images from abundant normal patterns, incorporating a quantitative quality control mechanism. Using an ADI CD-SEM dataset, we conducted a sensitivity analysis by cropping original 1024 × 1024 micrographs into 512 × 512 and 256 × 256 inputs. Our results indicate that increasing the effective defect-area ratio is critical for improving generative stability and defect visibility. To ensure data integrity, we applied a screening protocol based on the Structural Similarity Index (SSIM) and a median absolute deviation noise metric to exclude low-fidelity outputs. When integrated into the training of XceptionNet classifiers, this filtered augmentation strategy yielded substantial performance gains on a held-out test set, specifically improving the Recall and F1 score while maintaining a near-ceiling AUC. These results demonstrate that controlled CycleGAN augmentation, coupled with objective quality filtering, effectively mitigates class imbalance constraints and significantly enhances the robustness of automated defect detection. Full article
(This article belongs to the Special Issue Deep Learning in Video and Image Processing: Challenges, Solutions, and Future Directions, 2nd Edition)
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Review

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43 pages, 1457 KB  
Review
Foundation Models for Volumetric Medical Imaging: Opportunities, Challenges, and Future Directions
by Tapotosh Ghosh, Farnaz Sheikhi, Junlin Guo, Yashbir Singh, Khaled Younis, Shiba Kuanar, Shahriar Faghani, Eduardo Moreno Judice de Mattos Farina, Yuankai Huo and Farhad Maleki
Electronics 2026, 15(6), 1245; https://doi.org/10.3390/electronics15061245 - 17 Mar 2026
Viewed by 1406
Abstract
Foundation models, known as the large-scale, pretrained models capable of generalizing across diverse tasks, have significantly advanced the field of medical image analysis. While most early applications focused on 2D modalities, the unique challenges and opportunities associated with volumetric medical imaging have recently [...] Read more.
Foundation models, known as the large-scale, pretrained models capable of generalizing across diverse tasks, have significantly advanced the field of medical image analysis. While most early applications focused on 2D modalities, the unique challenges and opportunities associated with volumetric medical imaging have recently attracted growing interest. This study provides a comprehensive overview of the current landscape of foundation models tailored for volumetric medical image analysis, with a focus on CT, MRI, and PET imaging. We examine key components of these models, including 3D architectures, training strategies, and supported modalities. In addition, we highlight their contribution to major clinical tasks such as classification and prediction, segmentation, image registration, quality enhancement, and visual question answering. Critical challenges of these models, including high computational cost, limited and less diverse 3D datasets, and domain adaptation, are discussed alongside the promising solutions and future research directions. By synthesizing recent advances in volumetric foundation models and outlining key technical and clinical challenges, this review provides a thorough roadmap toward the development of scalable, generalizable, and clinically applicable AI systems for volumetric medical images. Full article
(This article belongs to the Special Issue Deep Learning in Video and Image Processing: Challenges, Solutions, and Future Directions, 2nd Edition)
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