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Integration of AI in Signal and Image Processing

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 September 2025 | Viewed by 4246

Special Issue Editors


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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

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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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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 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. 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

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Published Papers (3 papers)

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Research

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20 pages, 3601 KiB  
Article
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 490
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 [...] Read more.
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)
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19 pages, 6426 KiB  
Article
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
Viewed by 910
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 [...] Read more.
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 (RPred2) 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)
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Review

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19 pages, 2333 KiB  
Review
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 1 | Viewed by 2135
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. [...] Read more.
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)
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