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Electronics
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Image Processing for Intelligent Electronics in Multimedia Systems
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Image Processing for Intelligent Electronics in Multimedia Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 December 2026 | Viewed by 2582

Editors

Dr. Peiya Li

E-Mail Website
Guest Editor
College of Cyber Security, Jinan University, Guangzhou 510632, China
Interests: multimedia security; image encryption; image retrieval; data hiding
Dr. Simying Ong

E-Mail Website
Guest Editor
Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
Interests: information security; image/visual and signal processing
Dr. Bingwen Feng

E-Mail Website
Guest Editor
College of Cyber Security, Jinan University, Guangzhou 510632, China
Interests: AI security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the rapidly evolving landscape of multimedia systems, image processing has emerged as a cornerstone technology, driving advancements in intelligent electronics and reshaping the way we interact with digital content. This interdisciplinary field combines computer vision, machine learning, and signal processing to enable devices to interpret, analyze, and manipulate visual data with unprecedented accuracy and efficiency. As multimedia systems become increasingly integrated into everyday life—from smart homes and autonomous vehicles to augmented reality and healthcare—the demand for sophisticated image processing techniques has never been greater.

This Special Issue invites scholars to explore the cutting-edge developments in image processing for intelligent electronics, focusing on the transformative role in multimedia systems. Topics of interest include, but are not limited to, the following:

  1. Real-Time Image and Video Processing: Techniques for enhancing the speed and efficiency of image and video analysis in resource-constrained environments, enabling seamless integration into real-time applications such as surveillance, gaming, and live broadcasting.
  2. Deep Learning and AI Solutions: Innovations in convolutional neural networks (CNNs), generative adversarial networks (GANs), and other AI-based models for tasks such as object detection, image segmentation, and style transfer, pushing the boundaries of what intelligent electronics can achieve.
  3. Multimodal Data Fusion: Integrating image data with other sensory inputs (e.g., audio, text, or sensor data) to create richer, more context-aware multimedia experiences, particularly in applications like virtual reality (VR) and augmented reality (AR).
  4. Edge Computing and Embedded Systems: Optimizing image processing algorithms for deployment on edge devices, ensuring low latency, energy efficiency, and robust performance in IoT-enabled multimedia systems.
  5. Security and Privacy in Multimedia: Addressing challenges related to data integrity, authentication, and privacy in image and video processing, particularly in sensitive applications like biometrics and medical imaging.
  6. Applications in Emerging Fields: Exploring novel uses of image processing in areas such as autonomous driving, smart cities, remote sensing, and telemedicine, where intelligent electronics play a pivotal role in decision-making and automation.

By contributing to this Special Issue, scholars will have the opportunity to showcase their research to a global audience of experts, fostering collaboration and driving innovation in this dynamic field. We welcome original research articles, review papers, and case studies that highlight both theoretical advancements and practical applications. Together, we can unlock the full potential of image processing in intelligent electronics, shaping the future of multimedia systems and beyond.

Join us in this exciting journey to redefine the boundaries of technology and create a smarter, more connected world. Submit your paper today and be part of the next wave of breakthroughs in image processing for intelligent electronics in multimedia systems!

Dr. Peiya Li
Dr. Simying Ong
Dr. Bingwen Feng
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. 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

  • image processing
  • intelligent electronics
  • multimedia systems
  • multimedia security and privacy
  • computer vision
  • deep learning
  • edge computing
  • multimodal data fusion
  • AI-driven solutions
  • emerging applications

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Published Papers (2 papers)

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Research

27 pages, 818 KB  
Article
Active Defense for Deepfakes Using Watermark-Guided Original Face Recovery
by Yizhi Guo, Ziqiao Liu, Yantao Li and Bingwen Feng
Electronics 2026, 15(3), 625; https://doi.org/10.3390/electronics15030625 - 2 Feb 2026
Viewed by 1148
Abstract
At present, active defense strategies based on digital watermarking mainly rely on post-event watermark extraction, which verifies the occurrence of deepfake events by measuring the degree of watermark degradation, or on adversarial watermarks to interfere with image generation. To overcome these limitations, we [...] Read more.
At present, active defense strategies based on digital watermarking mainly rely on post-event watermark extraction, which verifies the occurrence of deepfake events by measuring the degree of watermark degradation, or on adversarial watermarks to interfere with image generation. To overcome these limitations, we propose a unified watermarking framework that can restore the original content of images tampered with by deepfakes. This scheme integrates three core components: an encoder for watermark pre-embedding, a decoder for robust watermark extraction, and a face restorer for watermark-guided image restoration. Numerous experiments have shown that this method has achieved good results in terms of extraction accuracy and recovery performance, thereby verifying the effectiveness of this approach. Full article
(This article belongs to the Special Issue Image Processing for Intelligent Electronics in Multimedia Systems)
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18 pages, 14209 KB  
Article
A Real-Time Improved YOLOv10 Model for Small and Multi-Scale Ground Target Detection in UAV LiDAR Range Images of Complex Scenes
by Yu Zhai, Ziyi Zhang, Sen Xie, Chunsheng Tong, Xiuli Luo, Xuan Li, Liming Wang and Yingliang Zhao
Electronics 2026, 15(1), 211; https://doi.org/10.3390/electronics15010211 - 1 Jan 2026
Cited by 2 | Viewed by 938
Abstract
Low-altitude Unmanned Aerial Vehicle (UAV) detection using LiDAR range images faces persistent challenges. These include sparse features for long-range targets, large scale variations caused by viewpoint changes, and severe interference from complex backgrounds. To address these issues, we propose an improved detection framework [...] Read more.
Low-altitude Unmanned Aerial Vehicle (UAV) detection using LiDAR range images faces persistent challenges. These include sparse features for long-range targets, large scale variations caused by viewpoint changes, and severe interference from complex backgrounds. To address these issues, we propose an improved detection framework based on YOLOv10. First, we design a Swin-Conv hybrid module that combines sparse attention with deformable convolution. This module enables the network to focus on informative regions and adapt to target geometry. These capabilities jointly strengthen feature extraction for sparse, long-range targets. Second, we introduce Attentional Feature Fusion (AFF) in the neck to replace naïve feature concatenation. AFF employs multi-scale channel attention to softly select and adaptively weight features from different levels, improving robustness to multi-scale targets. In addition, we systematically study how the viewpoint distribution in the training set affects performance. The results show that moderately increasing the proportion of low-elevation-view samples significantly improves detection accuracy. Experiments on a self-built simulated LiDAR range-image dataset demonstrate that our method achieves 88.96% mAP at 54.2 FPS, which is 4.78 percentage points higher than the baseline. Deployment on the Jetson Orin Nano edge device further validates the model’s potential for real-time applications. The proposed method remains robust under noise and complex backgrounds. The proposed approach achieves an effective balance between detection accuracy and computational efficiency, providing a reliable solution for real-time target detection in complex low-altitude environments. Full article
(This article belongs to the Special Issue Image Processing for Intelligent Electronics in Multimedia Systems)
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