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Electronics
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AI-Driven Digital Image Processing: Latest Advances and Prospects
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AI-Driven Digital Image Processing: Latest Advances and Prospects

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

Deadline for manuscript submissions: closed (15 December 2024) | Viewed by 6464

Special Issue Editors

Dr. Guanzhou Chen

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Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China
Interests: remote sensing image analysis; 3D reconstruction and semantic perception
Special Issues, Collections and Topics in MDPI journals
Dr. Kun Zhu

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Guest Editor
Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China
Interests: object detection; object tracking; remote sensing image processing
Dr. Jinzhou Cao

E-Mail Website
Guest Editor
College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China
Interests: urban data science; urban analytics; Geo-AI; human mobility

Special Issue Information

Dear Colleagues,

Digital Image Processing significantly impacts our ability to handle visual information and has become crucial in the development of technology across varied fields. This area's recent progress is mainly fueled by advancements in artificial intelligence (AI), especially deep learning, driving a shift towards more intelligent image processing systems. These AI-driven systems have improved applications in autonomous vehicles, medical diagnostics, remote sensing and space exploration by enabling sophisticated image analysis for decision-making and research.

The emergence of deep learning has introduced efficient methods that accurately handle complex datasets, supporting better data interpretation. AI integration with image processing enhances our capabilities in various sectors, from environmental monitoring to improving vehicle perception systems and medical diagnostic accuracy.

However, the rise of AI in image processing brings challenges, such as managing large data volumes, ensuring model reliability, and addressing privacy and bias concerns. These issues highlight the need for continuous innovation and a collaborative platform for sharing the latest developments in Digital Image Processing.

To address these needs, the "AI-Driven Digital Image Processing: Latest Advances and Prospects" Special Issue aims to gather and disseminate recent advancements, methodologies, and ideas in the field, with the hope of promoting collaboration among experts to overcome current challenges.

Articles and reviews on image processing are welcome. The specific topics to be addressed include, but are not limited to, the following:

  • Semantic segmentation and instance segmentation;
  • UAV/UAS/BEV/Satellite 3D reconstruction and semantic perception;
  • Multimodal Large Models;
  • Multimodal data collaborative analysis and processing;
  • Video processing;
  • Image processing for Medical Imaging and Deep Space Exploration;
  • Benchmark datasets for image processing and large models;
  • Semi-/Self-supervised image analysis;
  • Few-shot/Zero-shot image processing;
  • Image-processing-based applications;
  • Multi-source sensing data for urban analytics.

Dr. Guanzhou Chen
Dr. Kun Zhu
Dr. Jinzhou Cao
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. 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

  • deep learning
  • digital image processing
  • remote sensing
  • medical imaging
  • autonomous vehicles
  • deep space exploration
  • multimodal large models
  • 3D reconstruction
  • video processing

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Published Papers (3 papers)

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Research

25 pages, 2844 KiB  
Article
Real-Time Gesture-Based Hand Landmark Detection for Optimized Mobile Photo Capture and Synchronization
by Pedro Marques, Paulo Váz, José Silva, Pedro Martins and Maryam Abbasi
Electronics 2025, 14(4), 704; https://doi.org/10.3390/electronics14040704 - 12 Feb 2025
Viewed by 1529
Abstract
Gesture recognition technology has emerged as a transformative solution for natural and intuitive human–computer interaction (HCI), offering touch-free operation across diverse fields such as healthcare, gaming, and smart home systems. In mobile contexts, where hygiene, convenience, and the ability to operate under resource [...] Read more.
Gesture recognition technology has emerged as a transformative solution for natural and intuitive human–computer interaction (HCI), offering touch-free operation across diverse fields such as healthcare, gaming, and smart home systems. In mobile contexts, where hygiene, convenience, and the ability to operate under resource constraints are critical, hand gesture recognition provides a compelling alternative to traditional touch-based interfaces. However, implementing effective gesture recognition in real-world mobile settings involves challenges such as limited computational power, varying environmental conditions, and the requirement for robust offline–online data management. In this study, we introduce ThumbsUp, which is a gesture-driven system, and employ a partially systematic literature review approach (inspired by core PRISMA guidelines) to identify the key research gaps in mobile gesture recognition. By incorporating insights from deep learning–based methods (e.g., CNNs and Transformers) while focusing on low resource consumption, we leverage Google’s MediaPipe in our framework for real-time detection of 21 hand landmarks and adaptive lighting pre-processing, enabling accurate recognition of a “thumbs-up” gesture. The system features a secure queue-based offline–cloud synchronization model, which ensures that the captured images and metadata (encrypted with AES-GCM) remain consistent and accessible even with intermittent connectivity. Experimental results under dynamic lighting, distance variations, and partially cluttered environments confirm the system’s superior low-light performance and decreased resource consumption compared to baseline camera applications. Additionally, we highlight the feasibility of extending ThumbsUp to incorporate AI-driven enhancements for abrupt lighting changes and, in the future, electromyographic (EMG) signals for users with motor impairments. Our comprehensive evaluation demonstrates that ThumbsUp maintains robust performance on typical mobile hardware, showing resilience to unstable network conditions and minimal reliance on high-end GPUs. These findings offer new perspectives for deploying gesture-based interfaces in the broader IoT ecosystem, thus paving the way toward secure, efficient, and inclusive mobile HCI solutions. Full article
(This article belongs to the Special Issue AI-Driven Digital Image Processing: Latest Advances and Prospects)
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37 pages, 6293 KiB  
Article
KidneyNet: A Novel CNN-Based Technique for the Automated Diagnosis of Chronic Kidney Diseases from CT Scans
by Saleh Naif Almuayqil, Sameh Abd El-Ghany, A. A. Abd El-Aziz and Mohammed Elmogy
Electronics 2024, 13(24), 4981; https://doi.org/10.3390/electronics13244981 - 18 Dec 2024
Viewed by 1542
Abstract
This study presents KidneyNet, an innovative computer-aided diagnosis (CAD) system designed to identify chronic kidney diseases (CKDs), such as kidney stones, cysts, and tumors, in CT scans. KidneyNet utilizes a convolutional neural network (CNN) structure consisting of eight convolutional layers, three pooling layers, [...] Read more.
This study presents KidneyNet, an innovative computer-aided diagnosis (CAD) system designed to identify chronic kidney diseases (CKDs), such as kidney stones, cysts, and tumors, in CT scans. KidneyNet utilizes a convolutional neural network (CNN) structure consisting of eight convolutional layers, three pooling layers, a flattening layer, and two fully connected layers. Small filters enhance computational efficiency by reducing the number of parameters and minimizing the risk of overfitting compared to larger filters. The model captures more complex and abstract features as data move through the layers. The initial layers identify basic patterns, while the deeper layers focus on more intricate representations. KidneyNet aims to enhance the efficiency and accuracy of kidney disease diagnosis. Additionally, the model incorporates the gradient-weighted class activation mapping (Grad-CAM) algorithm, which helps to pinpoint affected areas in the scans. This feature improves interpretability, allowing clinicians to identify which regions the model deemed significant for detecting abnormalities such as tumors, cysts, or stones. Through extensive testing on a CT kidney dataset, KidneyNet demonstrated impressive performance metrics, with 99.88% accuracy, 99.92% specificity, 99.76% sensitivity, 99.58% precision, and an F1 score of 99.67%, outperforming existing models. This approach alleviates the diagnostic burden on radiologists and promotes early detection, potentially saving lives. This study highlights the critical role of advanced imaging analysis in addressing kidney conditions and emphasizes KidneyNet’s capability to deliver precise and cost-effective diagnoses. Full article
(This article belongs to the Special Issue AI-Driven Digital Image Processing: Latest Advances and Prospects)
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27 pages, 9669 KiB  
Article
Using LSTM with Trajectory Point Correlation and Temporal Pattern Attention for Ship Trajectory Prediction
by Yi Zhou, Haitao Guo, Jun Lu, Zhihui Gong, Donghang Yu and Lei Ding
Electronics 2024, 13(23), 4705; https://doi.org/10.3390/electronics13234705 - 28 Nov 2024
Viewed by 1536
Abstract
Accurate ship trajectory prediction is crucial for real-time vessel position tracking and maritime safety management. However, existing methods for ship trajectory prediction encounter significant challenges. They struggle to effectively extract long-term and complex spatial–temporal features hidden within the data. Moreover, they often overlook [...] Read more.
Accurate ship trajectory prediction is crucial for real-time vessel position tracking and maritime safety management. However, existing methods for ship trajectory prediction encounter significant challenges. They struggle to effectively extract long-term and complex spatial–temporal features hidden within the data. Moreover, they often overlook correlations among multivariate dynamic features such as longitude (LON), latitude (LAT), speed over ground (SOG), and course over ground (COG), which are essential for precise trajectory forecasting. To address these pressing issues and fulfill the need for more accurate and comprehensive ship trajectory prediction, we propose a novel and integrated approach. Firstly, a Trajectory Point Correlation Attention (TPCA) mechanism is devised to establish spatial connections between trajectory points, thereby uncovering the local trends of trajectory point changes. Subsequently, a Temporal Pattern Attention (TPA) mechanism is introduced to handle the associations between multiple variables across different time steps and capture the dynamic feature correlations among trajectory attributes. Finally, a Great Circle Route Loss Function (GCRLoss) is constructed, leveraging the perception of the Earth’s curvature to deepen the understanding of spatial relationships and geographic information. Experimental results demonstrate that our proposed method outperforms existing ship trajectory prediction techniques, showing enhanced reliability in multi-step predictions. Full article
(This article belongs to the Special Issue AI-Driven Digital Image Processing: Latest Advances and Prospects)
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