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 11
Special Issue Editors
Interests: deep learning; machine Learning; video processing; image processing; Internet of Things, cybersecurity; embedded systems
Special Issues, Collections and Topics in MDPI journals
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
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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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