Application of Artificial Intelligence to Image Processing: Advantages and Prognosis

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

Deadline for manuscript submissions: 15 May 2025 | Viewed by 1776

Special Issue Editors


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Guest Editor
Department of Electrical and Computer Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
Interests: image processing; graph signal processing; network analysis; learning theory; wireless communications

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Guest Editor
Department of Data Science, New Jersey Institute of Technology, Newark, NJ 07102, USA
Interests: learning theory; high-dimensional data analysis; spatio-temporal data analysis; probability and statistical inference

Special Issue Information

Dear Colleagues,

The tremendous progress that has recently been made in artificial intelligence (AI) has stimulated numerous novel concepts and technologies in image processing, including multimodal data fusion, real-time image analysis, and high-dimensional image processing. The rapid development of AI has significantly enhanced the accuracy, speed, and automation of image processing tasks, such as object detection, segmentation, and classification, with broad applications in healthcare, autonomous driving, and edge computing. Among these technologies, one of the most exciting directions is the integration of generative AI in image processing, which introduces new possibilities for image generation, knowledge transfer, and image reconstruction. Another promising direction is physics-informed machine learning, which facilitates image processing with specific requirements in practical applications. Moreover, the emerging fields of self-supervised learning and federated learning provide efficient tools for image understanding and privacy protection, respectively.

This Special Issue aims to establish a platform for researchers to share their latest research outcomes and innovations in the emerging field of artificial intelligence for image processing. We welcome contributions to both theoretical fundamentals and practical applications related to AI-driven image processing. Particularly, we encourage submissions that explore the use of generative and self-supervised learning to address image synthesis and interpretation. We also encourage submissions focusing on physics-informed machine learning in image processing, integrating physical principles and data statistics. In addition, we strongly welcome submissions about the application of AI-assisted image processing to science and engineering problems with the consideration of efficiency and security, such as semantic communications, private distributed edge computing, future additive manufacturing, biomedical imaging, remote sensing, multimodal sensor fusion, and autonomous driving.

The scope of this Special Issue shall cover a wide range of topics in AI and image processing, including but not limited to the following:

  • Generative learning for image synthesis;
  • Self-supervised image processing;
  • Physics-informed machine learning for practical image processing;
  • Multimodal data fusion;
  • Federated learning for imaging data privacy;
  • Applications of AI-assisted image processing in scientific and engineering problems.

We invite original and high-quality submissions related to the theme of this Special Issue. We believe this Special Issue will provide good opportunities for researchers to share their latest outcomes and insightful views on the advantages and prognosis of the application of AI to image processing, which shall also inspire future AI technologies for image processing, together with its practical applications.

Dr. Songyang Zhang
Dr. Shuai Zhang
Guest Editors

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Keywords

  • image processing
  • generative learning
  • self-supervised learning
  • physics-informed machine learning
  • distributed learning

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Published Papers (2 papers)

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Research

14 pages, 7707 KiB  
Article
An Insurtech Platform to Support Claim Management Through the Automatic Detection and Estimation of Car Damage from Pictures
by Mohab Mahdy Helmy Atanasious, Valentina Becchetti, Alessandro Giuseppi, Antonio Pietrabissa, Vito Arconzo, Gerardo Gorga, Gonzalo Gutierrez, Ahmed Omar, Marco Pietrini, Meher Anvesh Rangisetty, Lorenzo Ricciardi Celsi, Federico Santini and Enrico Scianaro
Electronics 2024, 13(22), 4333; https://doi.org/10.3390/electronics13224333 - 5 Nov 2024
Viewed by 486
Abstract
Claims management is a complex process through which an insurance company or responsible entity addresses and handles compensation requests from policyholders who have suffered damage or losses. This process entails several stages, including the notification of the claim, damage assessment, settlement of compensation, [...] Read more.
Claims management is a complex process through which an insurance company or responsible entity addresses and handles compensation requests from policyholders who have suffered damage or losses. This process entails several stages, including the notification of the claim, damage assessment, settlement of compensation, and, if necessary, dispute resolution. Fair, transparent and timely claims management is crucial for maintaining policyholders’ trust while also limiting the financial impact on the insurer. Technological innovations, such as the use of artificial intelligence and automation, are positively influencing this sector, enabling faster and more effective claims management. This study reports on Insoore AI, an insurtech solution that aims to automate a portion of claims management by integrating a computer vision solution based on some latest developments in deep learning to automatically recognize and localize car damage from user-provided pictures. Full article
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21 pages, 20204 KiB  
Article
Enhanced Satellite Analytics for Mussel Platform Census Using a Machine-Learning Based Approach
by Fernando Martín-Rodríguez, Luis M. Álvarez-Sabucedo, Juan M. Santos-Gago and Mónica Fernández-Barciela
Electronics 2024, 13(14), 2782; https://doi.org/10.3390/electronics13142782 - 15 Jul 2024
Viewed by 773
Abstract
Mussel platforms are big floating structures made of wood (normally about 20 m × 20 m or even a bit larger) that are used for aquaculture. They are used for supporting the growth of mussels in suitable marine waters. These structures are very [...] Read more.
Mussel platforms are big floating structures made of wood (normally about 20 m × 20 m or even a bit larger) that are used for aquaculture. They are used for supporting the growth of mussels in suitable marine waters. These structures are very common near the Galician coastline. For their maintenance and tracking, it is quite convenient to be able to produce a periodic census of these structures, including their current count and position. Images from Earth observation satellites are, a priori, a convenient choice for this purpose. This paper describes an application capable of automatically supporting such a census using optical images taken at different wavelength intervals. The images are captured by the two Sentinel 2 satellites (Sentinel 2A and Sentinel 2B, both from the Copernicus Project). The Copernicus satellites are run by the European Space Agency, and the produced images are freely distributed on the Internet. Sentinel 2 images include thirteen frequency bands and are updated every five days. In our proposal, remote-sensing normalized (differential) indexes are used, and machine-learning techniques are applied to multiband data. Different methods are described and tested. The results obtained in this paper are satisfactory and prove the approach is suitable for the intended purpose. In conclusion, it is worth noting that artificial neural networks turn out to be particularly good for this problem, even with a moderate level of complexity in their design. The developed methodology can be easily re-used and adapted for similar marine environments. Full article
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