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Electronics
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Application of Artificial Intelligence to Image Processing: Advantages and Prognosis
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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 2016

Special Issue Editors

Dr. Songyang Zhang

E-Mail Website
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
Dr. Shuai Zhang

E-Mail Website
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

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

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

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

Published Papers (3 papers)

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Research

21 pages, 3921 KiB  
Article
CFF-Net: Cross-Hierarchy Feature Fusion Network Based on Composite Dual-Channel Encoder for Surface Defect Segmentation
by Ke’er Qian, Xiaokang Ding, Xiaoliang Jiang, Yingyu Ji and Ling Dong
Electronics 2024, 13(23), 4714; https://doi.org/10.3390/electronics13234714 - 28 Nov 2024
Abstract
In industries spanning manufacturing to software development, defect segmentation is essential for maintaining high standards of product quality and reliability. However, traditional segmentation methods often struggle to accurately identify defects due to challenges like noise interference, occlusion, and feature overlap. To solve these [...] Read more.
In industries spanning manufacturing to software development, defect segmentation is essential for maintaining high standards of product quality and reliability. However, traditional segmentation methods often struggle to accurately identify defects due to challenges like noise interference, occlusion, and feature overlap. To solve these problems, we propose a cross-hierarchy feature fusion network based on a composite dual-channel encoder for surface defect segmentation, called CFF-Net. Specifically, in the encoder of CFF-Net, we design a composite dual-channel module (CDCM), which combines standard convolution with dilated convolution and adopts a dual-path parallel structure to enhance the model’s capability in feature extraction. Then, a dilated residual pyramid module (DRPM) is integrated at the junction of the encoder and decoder, which utilizes the expansion convolution of different expansion rates to effectively capture multi-scale context information. In the final output phase, we introduce a cross-hierarchy feature fusion strategy (CFFS) that combines outputs from different layers or stages, thereby improving the robustness and generalization of the network. Finally, we conducted comparative experiments to evaluate CFF-Net against several mainstream segmentation networks across three distinct datasets: a publicly available Crack500 dataset, a self-built Bearing dataset, and another publicly available SD-saliency-900 dataset. The results demonstrated that CFF-Net consistently outperformed competing methods in segmentation tasks. Specifically, in the Crack500 dataset, CFF-Net achieved notable performance metrics, including an Mcc of 73.36%, Dice coefficient of 74.34%, and Jaccard index of 59.53%. For the Bearing dataset, it recorded an Mcc of 76.97%, Dice coefficient of 77.04%, and Jaccard index of 63.28%. Similarly, in the SD-saliency-900 dataset, CFF-Net achieved an Mcc of 84.08%, Dice coefficient of 85.82%, and Jaccard index of 75.67%. These results underscore CFF-Net’s effectiveness and reliability in handling diverse segmentation challenges across different datasets. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence to Image Processing: Advantages and Prognosis)
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 496
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
(This article belongs to the Special Issue Application of Artificial Intelligence to Image Processing: Advantages and Prognosis)
►▼ Show Figures

Figure 1

Figure 1
<p>Block diagram of claims management.</p> Full article ">Figure 2
<p>Panel recognition results for multiple car models from various different angles.</p> Full article ">Figure 3
<p>Flowchart of the proposed AI system for automatic damage detection.</p> Full article ">Figure 4
<p>Damage classification: results obtained from our custom classifier for the four considered classes.</p> Full article ">Figure 5
<p>Performance curve of the experiments carried out.</p> Full article ">
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 783
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
(This article belongs to the Special Issue Application of Artificial Intelligence to Image Processing: Advantages and Prognosis)
►▼ Show Figures

Figure 1

Figure 1
<p>Sentinel 2 bands.</p> Full article ">Figure 2
<p>Platforms from the air.</p> Full article ">Figure 3
<p>Polygons of mussel rafts in a Sentinel 2 image (Vigo estuary, Spain). Rafts detected inside the red ellipses.</p> Full article ">Figure 4
<p>General system flow diagram. Red dotted lines indicate alternate methods.</p> Full article ">Figure 5
<p>NDWI is represented in grayscale. (<b>a</b>) Entire study area, (<b>b</b>) zoom of the rafts.</p> Full article ">Figure 6
<p>Histogram of the NDWI image (logarithmic scale). <span class="html-italic">X</span>-axis: NDWI image value, <span class="html-italic">Y</span>-axis: value repetition count.</p> Full article ">Figure 7
<p>Confusion matrix (only for the 15% test samples).</p> Full article ">Figure 8
<p>Detection with neural networks.</p> Full article ">Figure 9
<p>Confusion matrix (only for 15% of test samples).</p> Full article ">Figure 10
<p>Example of a false positive (bridge of “Ría de Noia”). (<b>a</b>) Result of detection, (<b>b</b>) aerial in situ photo showing the structure.</p> Full article ">Figure 11
<p>Example of result (crop of Vigo estuary). (<b>a</b>) Entire area of study, (<b>b</b>) area of the rafts.</p> Full article ">Figure 12
<p>Platforms in the delta of the river Ebro.</p> Full article ">Figure 13
<p>Use of BOA correction (Pontevedra estuary).</p> Full article ">Figure A1
<p>Band 1 (UV, coastal aerosol).</p> Full article ">Figure A2
<p>RGB composition of visible bands.</p> Full article ">Figure A3
<p>(<b>a</b>) 705 nm Red Edge, (<b>b</b>) 740 nm Red Edge, (<b>c</b>) 783 nm Red Edge.</p> Full article ">Figure A4
<p>NIR.</p> Full article ">Figure A5
<p>865 nm Red Edge.</p> Full article ">Figure A6
<p>(<b>a</b>) Water vapor, (<b>b</b>) Cirrus.</p> Full article ">Figure A7
<p>(<b>a</b>) SWIR at 1610 nm, (<b>b</b>) SWIR at 2190 nm.</p> Full article ">
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