Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

Journal Browser

Integration of AI in Signal and Image Processing

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 June 2026 | Viewed by 34259

Share This Special Issue

Special Issue Editors

Dr. Abdussalam Elhanashi

E-Mail Website
Guest Editor

Dipartimento di Ingegneria dell'Informazione, Università di Pisa, Via Girolamo Caruso, 16, 56122 Pisa, Italia
Interests: deep learning; video/imaging processing and coding; surveillance and real-time applications; medical images; low-cost embedded systems
Special Issues, Collections and Topics in MDPI journals

Dr. Pierpaolo Dini

E-Mail Website
Guest Editor

Department of Information Engineering, University of Pisa, 56122 Pisa, Italy
Interests: MATLAB simulation; cybersecurity; control theory; system modeling; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue titled “Integration of AI in Signal and Image Processing” delves into the cutting-edge methodologies and innovative applications that are reshaping the landscape of signal and image processing. This comprehensive compilation brings together research and developments from a diverse array of topics, emphasizing both theoretical advancements and practical implementations.

Key themes explored in this Special Issue include the following:

Enhanced algorithms: This Special Issue highlights novel algorithms designed to improve the accuracy and efficiency of signal and image processing tasks. These include advanced filtering techniques, noise reduction methods, and algorithms for signal reconstruction that push the boundaries of current capabilities.
Machine learning and AI integration: The integration of machine learning and artificial intelligence (AI) has revolutionized signal and image processing. This Special Issue features articles on how deep learning, neural networks, and other AI-driven techniques are being employed to solve complex problems, offering enhanced performance over traditional methods.
Medical imaging: One of the standout applications discussed is in the field of medical imaging. Cutting-edge techniques for image segmentation, diagnosis support, and the enhancement of medical images are presented, showcasing how these advancements contribute to the more accurate and early diagnosis of medical conditions.
Real-time processing: The demand for real-time processing capabilities is ever-increasing. This Special Issue explores methods and technologies that enable the real-time analysis of signals and images, which is crucial for applications such as autonomous vehicles, surveillance systems, and real-time monitoring.
Multidimensional signal processing: Expanding beyond traditional 2D images, this Special Issue examines processing techniques for multidimensional data, including 3D and even higher-dimensional data sets. This is particularly relevant for applications in scientific research and advanced imaging technologies.
Emerging applications: This Special Issue also looks at emerging applications of signal and image processing in fields such as remote sensing, environmental monitoring, and multimedia. These articles illustrate the broad impact and potential of advanced processing techniques across various industries.

Dr. Abdussalam Elhanashi
Dr. Pierpaolo Dini
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. Applied Sciences 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

signal processing
image processing
algorithms
machine learning
artificial intelligence
deep learning
neural networks
noise reduction
signal reconstruction
filtering techniques
medical imaging
image segmentation
diagnosis support
real-time processing
autonomous vehicles
surveillance systems
multidimensional data
3D imaging
remote sensing
environmental monitoring
multimedia
pattern recognition
feature extraction
data enhancement
computational imaging
image analysis
signal enhancement
adaptive filtering
biomedical applications
data fusion

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (8 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

20 pages, 2215 KB

Open AccessArticle

Frame Selection Strategies for Video Deepfake Detection: Benchmarking Accuracy and Runtime Trade-Offs

by Artūras Serackis, Mindaugas Jankauskas, Anastasija Grubinskienė and Vytautas Abromavičius

Appl. Sci. 2026, 16(11), 5364; https://doi.org/10.3390/app16115364 - 27 May 2026

Viewed by 162

Abstract

This study evaluates frame selection during inference as an independent factor in video deepfake detection while keeping the downstream detectors fixed. We compare twelve frame selection strategies, ranging from simple temporal and quality baselines to landmark aware policies, using four validated pretrained detectors: Self-Blended Images (SBIs), Frequency-Enhanced Self-Blended Images (FSBIs), Generative Convolutional Vision Transformer (GenConViT), and GenD. The primary experiment is a complete factorial benchmark with 300 videos and five frame budgets (2, 4, 8, 16, and 32 selected frames), which provides the reference results at 32 frames. To address sample size limitations, an additional validation experiment uses a deduplicated split of 1180 Celeb-DF++ and FaceForensics++ videos, with complete results for 2, 4, and 8 selected frames and a reported subset for 16 selected frames. In the complete 300-video benchmark, 32 frames achieved the strongest average AUC, while 8 and 16 frames recovered most of the attainable performance with lower runtime. The best single validated configuration was GenD with Shot-aware sampling at 32 frames, yielding an AUC of 0.9607 and a balanced accuracy of 0.9133. The study therefore does not claim that smaller budgets universally outperform 32 frames; instead, it quantifies the tradeoff between accuracy and runtime and shows that frame selection remains a meaningful design variable under constrained inference budgets. Full article

(This article belongs to the Special Issue Integration of AI in Signal and Image Processing)

► Show Figures

Figure 1

25 pages, 7566 KB

Open AccessArticle

A Metrologically Guided YOLOv12 Framework with Augmentation-Free Training and Two-Phase Optimization for Multiclass Tomato Leaf Disease Detection

by Ihtisham Ul Haq, Francesco Felicetti, Domenico Luca Carnì and Francesco Lamonaca

Appl. Sci. 2026, 16(5), 2252; https://doi.org/10.3390/app16052252 - 26 Feb 2026

Viewed by 596

Abstract

Tomatoes are highly vulnerable to a wide range of leaf diseases, which significantly reduce agricultural yield and quality. Timely and precise detection of these diseases is essential for sustainable crop management and food security. This study analyzes configuration-level bidirectional multi-scale feature propagation within the native YOLOv12-s architecture, with emphasis on architectural behavior under controlled experimental conditions. The computational topology and parameterization of YOLOv12 are preserved, while bidirectional feature aggregation is activated at configuration level to examine its influence on cross-scale semantic consistency and localization reliability. The framework was trained and evaluated on a curated dataset of 4030 annotated RGB images spanning ten tomato leaf disease categories. All models were trained under an augmentation-free protocol and unified evaluation settings to isolate architectural effects from data-driven performance inflation. Under these controlled conditions, configuration-level bidirectional activation yields measurable improvements in detection consistency and spatial agreement while maintaining identical model complexity. Performance is evaluated using mAP, precision, recall, F1-score, and error-type decomposition within a measurement-consistency framework. The proposed configuration achieves 95.9% mAP@50 and 87.1% mAP@50–95 under identical experimental conditions, providing empirical evidence that topology-preserving feature routing influences multi-scale semantic stability in lesion detection. Full article

(This article belongs to the Special Issue Integration of AI in Signal and Image Processing)

► Show Figures

Figure 1

14 pages, 1583 KB

Open AccessArticle

Reference-Free Evaluation Metric for Fine-Grained 3D Shape Editing

by JiangDong Miao, Bisser Raytchev, Takuji Nakashima, Takenori Hiraoka, Keigo Shimizu, Yanlei Gu and Toru Higaki

Appl. Sci. 2025, 15(24), 13023; https://doi.org/10.3390/app152413023 - 10 Dec 2025

Viewed by 807

Abstract

Evaluating the quality of fine-grained 3D shape editing, such as adjusting a vehicle’s roof length or wheelbase, is essential for assessing generative models but remains challenging. Most existing metrics depend on auxiliary regressors or large-scale human evaluations, which may introduce bias, reduce reproducibility, and increase evaluation cost. To address these issues, a reference-free metric for evaluating fine-grained 3D shape editing is proposed. The method is based on the Rich-Attribute Sufficiency Assumption (RASA), which posits that when a geometric attribute set is sufficiently comprehensive, models with the same attribute vector should exhibit nearly identical shapes. Following this assumption, the dataset itself serves as a validation source: each source model is edited to match a small set of target attribute vectors, and the post-editing similarity to the targets reflects the editor’s accuracy and stability. Reproducible indicators are defined, including mean similarity, variation across targets, and calibration with respect to attribute distance. Empirical validation demonstrates the effectiveness of the proposed metric, showing approximately 9% degradation under semantic perturbations and less than 2% variation across different target-sampling settings, confirming both its discriminative sensitivity and robustness. This framework provides a low-cost, regressor-free benchmark for fine-grained editing and establishes its applicability through an explicit assumption and evaluation protocol. Full article

(This article belongs to the Special Issue Integration of AI in Signal and Image Processing)

► Show Figures

Figure 1

20 pages, 3601 KB

Open AccessArticle

Full-Scale Piano Score Recognition

by Xiang-Yi Zhang and Jia-Lien Hsu

Appl. Sci. 2025, 15(5), 2857; https://doi.org/10.3390/app15052857 - 6 Mar 2025

Viewed by 2051

Abstract

Sheet music is one of the most efficient methods for storing music. Meanwhile, a large amount of sheet music-image data is stored in paper form, but not in a computer-readable format. Therefore, digitizing sheet music is an essential task, such that the encoded music object could be effectively utilized for tasks such as editing or playback. Although there have been a few studies focused on recognizing sheet music images with simpler structures—such as monophonic scores or more modern scores with relatively simple structures, only containing clefs, time signatures, key signatures, and notes—in this paper we focus on the issue of classical sheet music containing dynamics symbols and articulation signs, more than only clefs, time signatures, key signatures, and notes. Therefore, this study augments the data from the GrandStaff dataset by concatenating single-line scores into multi-line scores and adding various classical music dynamics symbols not included in the original GrandStaff dataset. Given a full-scale piano score in pages, our approach first applies three YOLOv8 models to perform the three tasks: 1. Converting a full page of sheet music into multiple single-line scores; 2. Recognizing the classes and absolute positions of dynamics symbols in the score; and 3. Finding the relative positions of dynamics symbols in the score. Then, the identified dynamics symbols are removed from the original score, and the remaining score serves as the input into a Convolutional Recurrent Neural Network (CRNN) for the following steps. The CRNN outputs KERN notation (KERN, a core pitch/duration representation for common practice music notation) without dynamics symbols. By combining the CRNN output with the relative and absolute position information of the dynamics symbols, the final output is obtained. The results show that with the assistance of YOLOv8, there is a significant improvement in accuracy. Full article

(This article belongs to the Special Issue Integration of AI in Signal and Image Processing)

► Show Figures

Figure 1

19 pages, 6426 KB

Open AccessArticle

The Influence and Compensation of Environmental Factors (pH, Temperature, and Conductivity) on the Detection of Chemical Oxygen Demand in Water by UV-Vis Spectroscopy

by Jingwei Li, Yipei Ding, Yijing Lu, Jia Liu, Chenxuan Zhou and Zhiyu Shao

Appl. Sci. 2025, 15(4), 1694; https://doi.org/10.3390/app15041694 - 7 Feb 2025

Cited by 3 | Viewed by 5121

Abstract

In recent years, ultraviolet-visible (UV-Vis) spectroscopy has become one of the important methods used to measure water chemical oxygen demand (COD). However, environmental factors (pH, temperature, conductivity, etc.) can interfere with spectral information, thereby influencing the stability and accuracy of COD detection. The three environmental factors that influence UV-Vis spectroscopy were researched in this study. Considering the complexity of environmental factors, a data fusion method is proposed to compensate for the influence of three environmental factors simultaneously. This data fusion method is based on the weighted superposition of the spectrum and three environmental factors. A COD prediction model was established by fusing spectral feature wavelengths and environmental factors to reduce the influence of environmental factors on COD detection. Through the proposed data fusion method, the accuracy of COD detection based on UV-Vis spectroscopy has been improved. The determination coefficient of prediction (

R_{P r e d}^{2}

) reaches 0.9602, and the root mean square error of prediction (RMSEP) reaches 3.52. Full article

(This article belongs to the Special Issue Integration of AI in Signal and Image Processing)

► Show Figures

Figure 1

Review

Jump to: Research

34 pages, 863 KB

Open AccessReview

Secure Communication Protocols and AI-Based Anomaly Detection in UAV-GCS

by Dimitrios Papathanasiou, Evangelos Zacharakis, John Liaperdos, Theodore Kotsilieris, Ioannis E. Livieris and Konstantinos Ioannou

Appl. Sci. 2026, 16(7), 3339; https://doi.org/10.3390/app16073339 - 30 Mar 2026

Viewed by 1200

Abstract

Unmanned Aerial Vehicles (UAVs) are increasingly integrated into critical applications ranging from logistics and agriculture to defence and security operations, surveillance and emergency response. At the core of these systems lies the communication link between the UAV and its ground control station (GCS), which serves as the backbone for command, control and data exchange. However, communications links remain highly vulnerable to cyber-threats, including eavesdropping, signal falsification, radio frequency interference (RFI) and hijacking. These risks highlight the urgent need for secure communication protocols and effective defence mechanisms capable of protecting data confidentiality, integrity, availability and authentication. This study performs a comprehensive survey of secure UAV-GCS communication protocols and artificial intelligence (AI)-driven intrusion detection techniques. Initially, we review widely used communication protocols, examining their security features, vulnerabilities and existing countermeasures. Accordingly, a taxonomy of UAV-GCS security threats is proposed, structured around confidentiality, integrity, availability and authentication and map these threats to relevant attacks and defences. In parallel, our study examines state-of-the-art intrusion detection systems for UAVs, while particular emphasis is placed on emerging methods such as deep learning, federated learning, tiny machine learning and explainable AI, which hold promise for lightweight and real-time threat detection. The survey concludes by identifying open challenges, including resource constraints, lack of standardised secure protocols, scarcity of UAV-specific datasets and the evolving sophistication of attackers. Finally, we outline research directions for next-generation UAV architectures that integrate secure communication protocols with AI-based anomaly detection to achieve resilient and intelligent drone ecosystems. Full article

(This article belongs to the Special Issue Integration of AI in Signal and Image Processing)

► Show Figures

Figure 1

31 pages, 53190 KB

Open AccessReview

Early Fire and Smoke Detection Using Deep Learning: A Comprehensive Review of Models, Datasets, and Challenges

by Abdussalam Elhanashi, Siham Essahraui, Pierpaolo Dini and Sergio Saponara

Appl. Sci. 2025, 15(18), 10255; https://doi.org/10.3390/app151810255 - 20 Sep 2025

Cited by 19 | Viewed by 13417

Abstract

The early detection of fire and smoke is essential for mitigating human casualties, property damage, and environmental impact. Traditional sensor-based and vision-based detection systems frequently exhibit high false alarm rates, delayed response times, and limited adaptability in complex or dynamic environments. Recent advances in deep learning and computer vision have enabled more accurate, real-time detection through the automated analysis of flame and smoke patterns. This paper presents a comprehensive review of deep learning techniques for fire and smoke detection, with a particular focus on convolutional neural networks (CNNs), object detection frameworks such as YOLO and Faster R-CNN, and spatiotemporal models for video-based analysis. We examine the benefits of these approaches in terms of improved accuracy, robustness, and deployment feasibility on resource-constrained platforms. Furthermore, we discuss current limitations, including the scarcity and diversity of annotated datasets, susceptibility to false alarms, and challenges in generalization across varying scenarios. Finally, we outline promising research directions, including multimodal sensor fusion, lightweight edge AI implementations, and the development of explainable deep learning models. By synthesizing recent advancements and identifying persistent challenges, this review provides a structured foundation for the design of next-generation intelligent fire detection systems. Full article

(This article belongs to the Special Issue Integration of AI in Signal and Image Processing)

► Show Figures

Figure 1

19 pages, 2333 KB

Open AccessReview

Detection of Manipulations in Digital Images: A Review of Passive and Active Methods Utilizing Deep Learning

by Paweł Duszejko, Tomasz Walczyna and Zbigniew Piotrowski

Appl. Sci. 2025, 15(2), 881; https://doi.org/10.3390/app15020881 - 17 Jan 2025

Cited by 14 | Viewed by 9326

Abstract

The modern society generates vast amounts of digital content, whose credibility plays a pivotal role in shaping public opinion and decision-making processes. The rapid development of social networks and generative technologies, such as deepfakes, significantly increases the risk of disinformation through image manipulation. This article aims to review methods for verifying images’ integrity, particularly through deep learning techniques, addressing both passive and active approaches. Their effectiveness in various scenarios has been analyzed, highlighting their advantages and limitations. This study reviews the scientific literature and research findings, focusing on techniques that detect image manipulations and localize areas of tampering, utilizing both statistical properties of images and embedded hidden watermarks. Passive methods, based on analyzing the image itself, are versatile and can be applied across a broad range of cases; however, their effectiveness depends on the complexity of the modifications and the characteristics of the image. Active methods, which involve embedding additional information into the image, offer precise detection and localization of changes but require complete control over creating and distributing visual materials. Both approaches have their applications depending on the context and available resources. In the future, a key challenge remains the development of methods resistant to advanced manipulations generated by diffusion models and further leveraging innovations in deep learning to protect the integrity of visual content. Full article

(This article belongs to the Special Issue Integration of AI in Signal and Image Processing)

► Show Figures

Journal Menu

Journal Browser

Integration of AI in Signal and Image Processing

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (8 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI