Digital Signal and Image Processing for Multimedia Technology, 2nd Edition

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

Deadline for manuscript submissions: 15 June 2026 | Viewed by 243

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Department of Information and Computer Engineering, Chung Yuan Christian University, Taoyuan City 320314, Taiwan
Interests: artificial intelligence; machine learning; deep learning; virtual reality
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Special Issue Information

Dear Colleagues,

Following the success of the first edition, we are proud to present the second edition of the Special Issue titled "Digital Signal and Image Processing for Multimedia Technology".

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 are now increasingly embedded in various edge devices for application in several fields, such as automobiles, transportation, education, and manufacturing to name a few.

For Special Issue, 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 looking for papers discussing a wide range of ideas, 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

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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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Published Papers (1 paper)

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Research

23 pages, 527 KB  
Article
Regularizing Temporal Explanations in Dynamic Neural Networks
by Dalius Navakauskas and Martynas Dumpis
Electronics 2026, 15(10), 2200; https://doi.org/10.3390/electronics15102200 - 20 May 2026
Abstract
Using attribution-based priors to improve the temporal interpretability and robustness of dynamic neural networks provides a computationally efficient method that does not alter the model structure during inference. We explore explanation-guided training for timeseries classification through the introduction of attribution-sensitive loss terms that [...] Read more.
Using attribution-based priors to improve the temporal interpretability and robustness of dynamic neural networks provides a computationally efficient method that does not alter the model structure during inference. We explore explanation-guided training for timeseries classification through the introduction of attribution-sensitive loss terms that serve as regularizers for the evolution of input relevance over time. The main contributions are the Temporal Relevance Smoothness Index (TRSI) and a ratio-based loss that reduces irregular step-to-step changes in channel-aggregated absolute relevance. TRSI is compared against temporal total-variation penalties computed using Layer-wise Relevance Propagation Total Variation (LRP-TV) and Integrated Gradients Total Variation (IG-TV). Experiments on a controlled three-class subset of the Korean University Human Activity Recognition (KU-HAR) dataset using a finite impulse response neural network (FIRNN) show that TRSI yields the strongest smoothness improvement, reducing the total variation of the aggregated relevance signal from 0.768 to 0.447 (41.8%), compared with 0.667 (LRP-TV) and 0.677 (IG-TV). Robustness tests indicate a clear advantage for TRSI under impulsive and white Gaussian test-time noise. Full article
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