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Electronics
Special Issues
Image and Video Processing for Emerging Multimedia Technology
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Image and Video Processing for Emerging 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 409

Special Issue Editors

Dr. Alireza Esmaeilzehi

E-Mail Website
Guest Editor
Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada
Interests: machine and deep learning; image processing; computer vision
Dr. Bilal Taha

E-Mail Website
Guest Editor
Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada
Interests: deep learning; generative models; computer vision

Special Issue Information

Dear Colleagues,

Image and video signal processing techniques play crucial roles in various multimedia technologies. Often, digital multimedia devices require efficient image and video signal processing algorithms to satisfy the user experiences. With emerging AI-enabled multimedia systems, the design and development of novel deep learning-based image and video processing algorithms are paramount. Specifically, the methods that can strike a balance between the performance of digital multimedia systems and their complexity and real-time processing capability have drawn the attention of research communities in recent years. These high-performance, low-complexity schemes can be designed and utilized for various multimedia technologies, including image and video restoration, segmentation, object detection, and retrieval.

In view of the importance of efficient AI-enabled digital multimedia systems, MDPI Electronics invites the researchers to submit their recent works for efficient multimedia systems using deep learning and generative AI to the Special Issue: Image and Video Processing for Emerging Multimedia Technology. The topics of this Special Issue include, but are not limited to:

  • Deep lightweight neural networks for image and video restoration, segmentation, object detection, and retrieval;
  • Generative diffusion models for image and video processing;
  • Multi-modal learning and multi-modal visual signal processing;
  • AI-enabled circuits and systems for image and video processing;
  • Real-time image and video processing systems;
  • Deep learning for image and video signal compression;
  • Deep learning-based image and video communication systems.

Dr. Alireza Esmaeilzehi
Dr. Bilal Taha
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

  • deep learning
  • generative diffusion models
  • multi-modal learning
  • image and video processing for multimedia

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

Published Papers (1 paper)

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Research

22 pages, 7958 KiB  
Article
Depth Upsampling with Local and Nonlocal Models Using Adaptive Bandwidth
by Niloufar Salehi Dastjerdi and M. Omair Ahmad
Electronics 2025, 14(8), 1671; https://doi.org/10.3390/electronics14081671 - 20 Apr 2025
Viewed by 105
Abstract
The rapid advancement of 3D imaging technology and depth cameras has made depth data more accessible for applications such as virtual reality and autonomous driving. However, depth maps typically suffer from lower resolution and quality compared to color images due to sensor limitations. [...] Read more.
The rapid advancement of 3D imaging technology and depth cameras has made depth data more accessible for applications such as virtual reality and autonomous driving. However, depth maps typically suffer from lower resolution and quality compared to color images due to sensor limitations. This paper introduces an improved approach to guided depth map super-resolution (GDSR) that effectively addresses key challenges, including the suppression of texture copying artifacts and the preservation of depth discontinuities. The proposed method integrates both local and nonlocal models within a structured framework, incorporating an adaptive bandwidth mechanism that dynamically adjusts guidance weights. Instead of relying on fixed parameters, this mechanism utilizes a distance map to evaluate patch similarity, leading to enhanced depth recovery. The local model ensures spatial smoothness by leveraging neighboring depth information, preserving fine details within small regions. On the other hand, the nonlocal model identifies similarities across distant areas, improving the handling of repetitive patterns and maintaining depth discontinuities. By combining these models, the proposed approach achieves more accurate depth upsampling with high-quality depth reconstruction. Experimental results, conducted on several datasets and evaluated using various objective metrics, demonstrate the effectiveness of the proposed method through both quantitative and qualitative assessments. The approach consistently delivers improved performance over existing techniques, particularly in preserving structural details and visual clarity. An ablation study further confirms the individual contributions of key components within the framework. These results collectively support the conclusion that the method is not only robust and accurate but also adaptable to a range of real-world scenarios, offering a practical advancement over current state-of-the-art solutions. Full article
(This article belongs to the Special Issue Image and Video Processing for Emerging Multimedia Technology)
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