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Information
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Artificial Intelligence for Signal, Image and Video Processing
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Artificial Intelligence for Signal, Image and Video Processing

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 772

Special Issue Editors

Dr. Seyed Sahand Mohammadi Ziabari

E-Mail Website
Guest Editor
College of Arts and Sciences, State University of New York—Empire State University, Saratoga Springs, NY 12866, USA
Interests: artificial intelligence; multi-agent based modeling; signal processing; data science
Dr. Ali Mohammed Mansoor Alsahag

E-Mail Website
Guest Editor
Faculty of Science, Graduate School of Informatics, University of Amsterdam, 1012 WP Amsterdam, The Netherlands
Interests: sensor data; data science; artificial intelligence; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce the launch of a forthcoming Special Issue in Information titled “Artificial Intelligence for Signal, Image and Video Processing.”

This Special Issue aims to explore cutting-edge developments in artificial intelligence as applied to the processing, analysis, and interpretation of signal, image, and video data. With the rapid evolution of deep learning, data-centric methods, and computational intelligence, AI continues to redefine how we approach complex tasks across a wide range of domains, including healthcare, security, multimedia, remote sensing, and autonomous systems.

We invite contributions that present novel models, frameworks, or applications that advance state-of-the-art signal, image, and video processing through AI techniques. Topics of interest include (but are not limited to) data-driven architectures, deep neural networks, generative models, feature learning, semantic segmentation, and real-time processing.

Both original research articles and high-quality review papers are welcome. Interdisciplinary work and submissions with strong experimental validation or practical relevance are particularly encouraged. We look forward to receiving your contributions and showcasing the latest advances at the intersection of AI and visual-signal processing.

Dr. Seyed Sahand Mohammadi Ziabari
Dr. Ali Mohammed Mansoor Alsahag
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 250 words) can be sent to the Editorial Office for assessment.

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. Information is an international peer-reviewed open access monthly 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 1800 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

  • artificial intelligence
  • signal and image processing
  • deep learning
  • computer vision

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Published Papers (1 paper)

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32 pages, 16687 KB  
Article
Toward Robust Human Pose Estimation Under Real-World Image Degradations and Restoration Scenarios
by Nada E. Elshami, Ahmad Salah, Amr Abdellatif and Heba Mohsen
Information 2025, 16(11), 970; https://doi.org/10.3390/info16110970 - 10 Nov 2025
Viewed by 565
Abstract
Human Pose Estimation (HPE) models have varied applications and represent a cutting-edge branch of study, whose systems such as MediaPipe (MP), OpenPose (OP), and AlphaPose (ALP) show marked success. One of these areas, however, that is inadequately researched is the impact of image [...] Read more.
Human Pose Estimation (HPE) models have varied applications and represent a cutting-edge branch of study, whose systems such as MediaPipe (MP), OpenPose (OP), and AlphaPose (ALP) show marked success. One of these areas, however, that is inadequately researched is the impact of image degradation on the accuracy of HPE models. Image degradation refers to images whose visual quality has been purposefully degraded by means of techniques, such as brightness adjustments (which can lead to an increase or a decrease in the intensity levels), geometric rotations, or resolution downscaling. The study of how these types of degradation impact the performance functionality of HPE models is an under-researched domaina that is a virtually unexplored area. In addition, current methods of the efficacy of existing image restoration techniques have not been rigorously evaluated and improving degraded images to a high quality has not been well examined in relation to improving HPE models. In this study, we explicitly clearly demonstrate a decline in the precision of the HPE model when image quality is degraded. Our qualitative and quantitative measurements identify a wide difference in performance in identifying landmarks as images undergo changes in brightness, rotation, or reductions in resolution. Additionally, we have tested a variety of existing image enhancement methods in an attempt to enhance their capability in restoring low-quality images, hence supporting improved functionality of HPE. Interestingly, for rotated images, using Pillow of OpenCV improves landmark recognition precision drastically, nearly restoring it to levels we see in high-quality images. In instances of brightness variation and in low-quality images, however, existing methods of enhancement fail to yield the improvements anticipated, highlighting a large direction of study that warrants further investigation and calls for additional research. In this regard, we proposed a wide-ranging system for classifying different types of image degradation systematically and for selecting appropriate algorithms for image restoration, in an effort to restore image quality. A key finding is that in a related study of current methods, the Tuned RotNet model achieves 92.04% accuracy, significantly outperforming the baseline model and surpassing the official RotNet model in predicting rotation degree of images, where the accuracy of official RotNet and Tuned RotNet classifiers were 61.59% and 92.04%, respectively. Furthermore, in an effort to facilitate future research and make it easier for other studies, we provide a new dataset of reference images and corresponding degenerated images, addressing a notable gap in controlled comparative studies, since currently there is a lack of controlled comparatives. Full article
(This article belongs to the Special Issue Artificial Intelligence for Signal, Image and Video Processing)
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