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Electronics
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Digital Signal and Image Processing for Multimedia Technology
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Digital Signal and Image Processing for Multimedia Technology

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 October 2025 | Viewed by 4520

Special Issue Editors

Dr. Chi-hung Chuang

E-Mail Website
Guest Editor
Department of Information and Computer Engineering, Chung Yuan Christian University, Taoyuan City 320314, Taiwan
Interests: artificial intelligence; machine learning; deep learning; virtual reality
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chih-Lung Lin

E-Mail Website
Guest Editor
Graduate Institute of Intelligent Robotics, Hwa Hsia University of Technology, New Taipei City 235, Taiwan
Interests: artificial intelligence; machine learning; image processing; biometrics; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Determining how to employ deep learning technology has become a primary research topic in numerous fields. These include, for example, image processing, computer vision, the Internet of Things, natural language processing, and multimedia processing. In addition, due to the increasing process power of electronic devices and the expansion of network transmission bandwidth, deep learning models have begun to be embedded in various edge devices for application in several fields, such as automobiles, transportation, education, manufacturing, and many others.

In this Special Issue, entitled "Deep Learning Applications in Image Processing and Edge Devices", we invite authors to submit original research articles and review articles related to the application of deep learning techniques in image processing and edge devices.

We are open to papers addressing a wide range of topics, including deep learning for image analysis problems, novel algorithms for applying deep learning to various computer vision domains, and innovative methods for porting deep learning models to edge devices.

Topics of interest in this Special Issue include, but are not limited to, the following:

  • Machine learning and deep learning for image processing and computer vision;
  • Deep learning algorithms for clustering and classification;
  • Deep learning algorithms for segmentation and data annotation;
  • Embedded multimedia applications for edge computing;
  • Novel applications in robotic vision and intelligent consumer electronics;
  • Application architecture of AI-based systems.

Dr. Chi-hung Chuang
Prof. Dr. Chih-Lung Lin
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
  • computer vision
  • deep learning
  • neural network
  • artificial intelligence
  • multimedia processing

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

Published Papers (3 papers)

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Research

19 pages, 2082 KB  
Article
Multi-Scale Grid-Based Semantic Surface Point Generation for 3D Object Detection
by Xin-Fu Chen, Chun-Chieh Lee, Jung-Hua Lo, Chi-Hung Chuang and Kuo-Chin Fan
Electronics 2025, 14(17), 3492; https://doi.org/10.3390/electronics14173492 - 31 Aug 2025
Viewed by 258
Abstract
3D object detection is a crucial technology in fields such as autonomous driving and robotics. As a direct representation of the 3D world, point cloud data plays a vital role in feature extraction and geometric representation. However, in real-world applications, point cloud data [...] Read more.
3D object detection is a crucial technology in fields such as autonomous driving and robotics. As a direct representation of the 3D world, point cloud data plays a vital role in feature extraction and geometric representation. However, in real-world applications, point cloud data often suffers from occlusion, resulting in incomplete observations and degraded detection performance. Existing methods, such as PG-RCNN, generate semantic surface points within each Region of Interest (RoI) using a single grid size. However, a fixed grid scale cannot adequately capture multi-scale features. A grid that is too small may miss fine structures—especially problematic when dealing with small or sparse objects—while a grid that is too large may introduce excessive background noise, reducing the precision of feature representation. To address this issue, we propose an enhanced PG-RCNN architecture with a Multi-Scale Grid Attention Module as the core contribution. This module improves the expressiveness of point features by aggregating multi-scale information and dynamically weighting features from different grid resolutions. Using a simple linear transformation, we generate attention weights to guide the model to focus on regions that contribute more to object recognition, while effectively filtering out redundant noise. We evaluate our method on the KITTI 3D object detection validation set. Experimental results show that, compared to the original PG-RCNN, our approach improves performance on the Cyclist category by 2.66% and 2.54% in the Moderate and Hard settings, respectively. Additionally, our approach shows more stable performance on small object detection tasks, with an average improvement of 2.57%, validating the positive impact of the Multi-Scale Grid Attention Module on fine-grained geometric modeling, and highlighting the efficiency and generalizability of our model. Full article
(This article belongs to the Special Issue Digital Signal and Image Processing for Multimedia Technology)
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18 pages, 15722 KB  
Article
PANDA: A Polarized Attention Network for Enhanced Unsupervised Domain Adaptation in Semantic Segmentation
by Chiao-Wen Kao, Wei-Ling Chang, Chun-Chieh Lee and Kuo-Chin Fan
Electronics 2024, 13(21), 4302; https://doi.org/10.3390/electronics13214302 - 31 Oct 2024
Viewed by 1719
Abstract
Unsupervised domain adaptation (UDA) focuses on transferring knowledge from the labeled source domain to the unlabeled target domain, reducing the costs of manual data labeling. The main challenge in UDA is bridging the substantial feature distribution gap between the source and target domains. [...] Read more.
Unsupervised domain adaptation (UDA) focuses on transferring knowledge from the labeled source domain to the unlabeled target domain, reducing the costs of manual data labeling. The main challenge in UDA is bridging the substantial feature distribution gap between the source and target domains. To address this, we propose Polarized Attention Network Domain Adaptation (PANDA), a novel approach that leverages Polarized Self-Attention (PSA) to capture the intricate relationships between the source and target domains, effectively mitigating domain discrepancies. PANDA integrates both channel and spatial information, allowing it to capture detailed features and overall structures simultaneously. Our proposed method significantly outperforms current state-of-the-art unsupervised domain adaptation (UDA) techniques for semantic segmentation tasks. Specifically, it achieves a notable improvement in mean intersection over union (mIoU), with a 0.2% increase for the GTA→Cityscapes benchmark and a substantial 1.4% gain for the SYNTHIA→Cityscapes benchmark. As a result, our method attains mIoU scores of 76.1% and 68.7%, respectively, which reflect meaningful advancements in model accuracy and domain adaptation performance. Full article
(This article belongs to the Special Issue Digital Signal and Image Processing for Multimedia Technology)
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15 pages, 3509 KB  
Article
Dense Feature Pyramid Deep Completion Network
by Xiaoping Yang, Ping Ni, Zhenhua Li and Guanghui Liu
Electronics 2024, 13(17), 3490; https://doi.org/10.3390/electronics13173490 - 2 Sep 2024
Viewed by 1295
Abstract
Most current point cloud super-resolution reconstruction requires huge calculations and has low accuracy when facing large outdoor scenes; a Dense Feature Pyramid Network (DenseFPNet) is proposed for the feature-level fusion of images with low-resolution point clouds to generate higher-resolution point clouds, which can [...] Read more.
Most current point cloud super-resolution reconstruction requires huge calculations and has low accuracy when facing large outdoor scenes; a Dense Feature Pyramid Network (DenseFPNet) is proposed for the feature-level fusion of images with low-resolution point clouds to generate higher-resolution point clouds, which can be utilized to solve the problem of the super-resolution reconstruction of 3D point clouds by turning it into a 2D depth map complementation problem, which can reduce the time and complexity of obtaining high-resolution point clouds only by LiDAR. The network first utilizes an image-guided feature extraction network based on RGBD-DenseNet as an encoder to extract multi-scale features, followed by an upsampling block as a decoder to gradually recover the size and details of the feature map. Additionally, the network connects the corresponding layers of the encoder and decoder through pyramid connections. Finally, experiments are conducted on the KITTI deep complementation dataset, and the network performs well in various metrics compared to other networks. It improves the RMSE by 17.71%, 16.60%, 7.11%, and 4.68% compared to the CSPD, Spade-RGBsD, Sparse-to-Dense, and GAENET. Full article
(This article belongs to the Special Issue Digital Signal and Image Processing for Multimedia Technology)
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