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Advanced Machine Learning Technologies and Their Applications in Intelligent Imaging...
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Advanced Machine Learning Technologies and Their Applications in Intelligent Imaging and Image Processing

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

Deadline for manuscript submissions: 15 September 2025 | Viewed by 3532

Special Issue Editors

Dr. Licheng Liu

E-Mail Website
Guest Editor
College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Interests: image processing; deep learning; machine learning
Dr. Yunyi Li

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Interests: signal processing; image restoration and fast imaging; deep learning; machine learning
Prof. Dr. Bing Tu

E-Mail Website
Guest Editor
Institute of Optics and Electronics, Nanjing University of Information Science and Technology, Nanjing, China
Interests: image processing; hyperspectral image anomaly detection; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent imaging and image processing is one of the fundamental tasks in the area of machine learning and artificial intelligence. Recently, continuous progress is being made in machine learning. Powered by advanced machine learning techniques, intelligent imaging and image processing has attracted increasing attention due to its wide range of applications, such as face image analysis, fast medical imaging, snapshot compressive imaging, hyperspectral image restoration, machine vision sensing, etc. Despite the promising results achieved using advanced machine learning technology and their increasing number of related applications and achievements, there remain several unsolved challenges regarding their practical applications, such as efficient image prior modeling, fast and robust large-scale optimization algorithms, etc. There is ample room for improvement in contemporary theories and methodologies for intelligent imaging, image processing, and their applications.

The aim of this Special Issue is to discuss new machine learning technologies and their applications in intelligent imaging and image processing. The topics include but not limited to new deep learning techniques; low-level image processing, restoration, and enhancement; intelligent sensing systems; signal processing; multi-sensor imaging fusion; and high-level image visions including image classification and recognition.

Dr. Licheng Liu
Dr. Yunyi Li
Prof. Dr. Bing Tu
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

  • intelligent imaging
  • image restoration
  • multi-sensor imaging fusion
  • face image analysis
  • hyperspectral image processing
  • advanced machine learning algorithms
  • new applications from novel AI technologies

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Published Papers (4 papers)

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Research

12 pages, 2532 KiB  
Article
Application of Deep Dilated Convolutional Neural Network for Non-Flat Rough Surface
by Chien-Ching Chiu, Yang-Han Lee, Wei Chien, Po-Hsiang Chen and Eng Hock Lim
Electronics 2025, 14(6), 1236; https://doi.org/10.3390/electronics14061236 - 20 Mar 2025
Viewed by 256
Abstract
In this paper, we propose a novel deep dilated convolutional neural network (DDCNN) architecture to reconstruct periodic rough surfaces, including their periodic length, dielectric constant, and shape. Historically, rough surface problems were addressed through optimization algorithms. However, these algorithms are computationally intensive, making [...] Read more.
In this paper, we propose a novel deep dilated convolutional neural network (DDCNN) architecture to reconstruct periodic rough surfaces, including their periodic length, dielectric constant, and shape. Historically, rough surface problems were addressed through optimization algorithms. However, these algorithms are computationally intensive, making the process very time-consuming. To resolve this issue, we provide measured scattered fields as training data for the DDCNN to reconstruct the periodic length, dielectric constant, and shape. The numerical results demonstrate that DDCNN can accurately reconstruct rough surface images under high noise levels. In addition, we also discuss the impacts of the periodic length and dielectric constant of the rough surface on the shape reconstruction. Notably, our method achieves excellent reconstruction results compared to DCNN even when the period and dielectric coefficient are unknown. Finally, it is worth mentioning that the trained network model completes the reconstruction process in less than one second, realizing efficient real-time imaging. Full article
(This article belongs to the Special Issue Advanced Machine Learning Technologies and Their Applications in Intelligent Imaging and Image Processing)
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20 pages, 8948 KiB  
Article
Detection of Sealing Surface of Electric Vehicle Electronic Water Pump Housings Based on Lightweight YOLOv8n
by Li Sun, Yi Shen, Jie Li, Weiyu Jiang, Xiang Bian and Mingxin Yuan
Electronics 2025, 14(2), 258; https://doi.org/10.3390/electronics14020258 - 9 Jan 2025
Viewed by 681
Abstract
Due to the characteristics of large size differences and shape variations in the sealing surface of electric vehicle electronic water pump housings, and the shortcomings of traditional YOLO defect detection models such as large volume and low accuracy, a lightweight defect detection algorithm [...] Read more.
Due to the characteristics of large size differences and shape variations in the sealing surface of electric vehicle electronic water pump housings, and the shortcomings of traditional YOLO defect detection models such as large volume and low accuracy, a lightweight defect detection algorithm based on YOLOv8n (You Only Look Once version 8n) is proposed for the sealing surface of electric vehicle electronic water pump housings. First, on the basis of introducing the MoblieNetv3 module, the YOLOv8n network structure is redesigned, which not only achieves network lightweighting but also improves the detection accuracy of the model. Then, DualConv (Dual Convolutional) convolution is introduced and the CMPDual (Cross Max Pooling Dual) module is designed to further optimize the detection model, which reduces redundant parameters and computational complexity of the model. Finally, in response to the characteristics of large size differences and shape variations in sealing surface defects, the Inner-WIoU (Inner-Wise-IoU) loss function is used instead of the CIoU (Complete-IoU) loss function in YOLOv8n, which improves the positioning accuracy of the defect area bounding box and further enhances the detection accuracy of the model. The ablation experiment based on the dataset constructed in this paper shows that compared with the YOLOv8n model, the weight of the proposed model is reduced by 61.9%, the computational complexity is reduced by 58.0%, the detection accuracy is improved by 9.4%, and the mAP@0.5 is improved by 6.9%. The comparison of detection results from different models shows that the proposed model has an average improvement of 6.9% in detection accuracy and an average improvement of 8.6% on mAP@0.5, which indicates that the proposed detection model effectively improves defect detection accuracy while ensuring model lightweighting. Full article
(This article belongs to the Special Issue Advanced Machine Learning Technologies and Their Applications in Intelligent Imaging and Image Processing)
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21 pages, 6186 KiB  
Article
Automatic Measurement of Comprehensive Skin Types Based on Image Processing and Deep Learning
by Jianghong Ran, Guolong Dong, Fan Yi, Li Li and Yue Wu
Electronics 2025, 14(1), 49; https://doi.org/10.3390/electronics14010049 - 26 Dec 2024
Viewed by 1324
Abstract
The skin serves as a physical and chemical barrier, effectively protecting us against the external environment. The Baumann Skin Type Indicator (BSTI) classifies skin into 16 types based on traits such as dry/oily (DO), sensitive/resistant (SR), pigmented/nonpigmented (PN), and wrinkle-prone/tight (WT). Traditional assessments [...] Read more.
The skin serves as a physical and chemical barrier, effectively protecting us against the external environment. The Baumann Skin Type Indicator (BSTI) classifies skin into 16 types based on traits such as dry/oily (DO), sensitive/resistant (SR), pigmented/nonpigmented (PN), and wrinkle-prone/tight (WT). Traditional assessments are time-consuming and challenging as they require the involvement of experts. While deep learning has been widely used in skin disease classification, its application in skin type classification, particularly using multimodal data, remains largely unexplored. To address this, we propose an improved Inception-v3 model incorporating transfer learning, based on the four-dimensional classification of the Baumann Skin Type Index (BSTI), which demonstrates outstanding accuracy. The dataset used in this study includes non-invasive physiological indicators, BSTI questionnaires, and skin images captured under various light sources. By comparing performance across different light sources, regions of interest (ROI), and baseline models, the improved Inception-v3 model achieved the best results, with accuracy reaching 91.11% in DO, 81.13% in SR, 91.72% in PN, and 74.9% in WT, demonstrating its effectiveness in skin type classification. This study surpasses traditional classification methods and previous similar research, offering a new, objective approach to measuring comprehensive skin types using multimodal and multi-light-source data. Full article
(This article belongs to the Special Issue Advanced Machine Learning Technologies and Their Applications in Intelligent Imaging and Image Processing)
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13 pages, 4672 KiB  
Article
A Four-Point Orientation Method for Scene-to-Model Point Cloud Registration of Engine Blades
by Duanjiao Li, Ying Zhang, Ziran Jia, Zhiyu Wang, Qiu Fang and Xiaogang Zhang
Electronics 2024, 13(23), 4634; https://doi.org/10.3390/electronics13234634 - 25 Nov 2024
Cited by 1 | Viewed by 826
Abstract
The use of 3D optical equipment for multi-view scanning is a promising approach to assessing the processing errors of engine blades. However, incomplete scanned point cloud data may impact the accuracy of point cloud registration (PCR). This paper proposes a four-point orientation point [...] Read more.
The use of 3D optical equipment for multi-view scanning is a promising approach to assessing the processing errors of engine blades. However, incomplete scanned point cloud data may impact the accuracy of point cloud registration (PCR). This paper proposes a four-point orientation point cloud registration method to improve the efficiency and accuracy of the coarse registration of turbine blades and prevent PCR failure. First, the point cloud is divided into four labeling blocks based on a principal component analysis. Second, keypoints are detected in each block based on their distance from the plane formed by the principal axes and described with a location-label descriptor based on their position. Third, a keypoint pair set is chosen based on the descriptor, and a suitable keypoint base is selected through singular value decomposition to obtain the final rigid transformation. To verify the effectiveness of the method, experiments are conducted on different blades. The results demonstrate the improved performance and efficiency of the proposed method of coarse registration for turbine blades. Full article
(This article belongs to the Special Issue Advanced Machine Learning Technologies and Their Applications in Intelligent Imaging and Image Processing)
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