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J. Imaging
Special Issues
AI-Driven Image and Video Understanding
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AI-Driven Image and Video Understanding

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "AI in Imaging".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 277

Special Issue Editors

Dr. Gongyang Li

E-Mail Website1 Website2
Guest Editor
School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
Interests: computer vision; multi-modal data processing; saliency detection; object/semantic segmentation; defect inspection
Dr. Xiaofei Zhou

E-Mail Website1 Website2
Guest Editor
School of Automation, Hangzhou Dianzi University, Hangzhou 310000, China
Interests: computer vision; image and video processing; saliency detection; defect detection
Dr. Yong Wu

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Tongji University, Shanghai 201804, China
Interests: machine learning; meta-learning; domain adaptation; image classification; gaze estimation; point cloud; saliency detection

Special Issue Information

Dear Colleague,

Artificial Intelligence (AI) has revolutionized image and video understanding, evolving from theoretical exploration to practical cornerstones across critical domains, including medical imaging for disease diagnosis, real-time perception in autonomous vehicles, intelligent content curation in multimedia platforms, and smart surveillance for public safety. This transformation has made AI indispensable, yet it also underscores the need to address emerging challenges in visual data analysis. This Special Issue aims to showcase cutting-edge research at the dynamic intersection of AI and visual content understanding.

Against the backdrop of explosive growth in unstructured visual data (e.g., high-resolution images, long-form videos) and rapid advancements in deep learning (e.g., transformer-based architectures), generative models (e.g., diffusion models), and multimodal fusion techniques, we actively seek submissions that push the boundaries of existing knowledge. Focused topics include novel neural architectures for image and video understanding, multi-model data processing, AI-driven computer vision, and AI-driven vision applications.  

We invite submissions that blend rigorous theoretical insights with robust empirical validation, explicitly addressing key challenges facing visual AI systems. By bringing together researchers from computer vision, machine learning, and application domains, this Special Issue seeks to accelerate cross-field knowledge exchange, drive technical innovation, and highlight how AI can unlock richer, more reliable insights from visual data. Ultimately, it aims to shape the future of AI-driven image and video understanding, fostering solutions that deliver tangible societal benefits.

Dr. Gongyang Li
Dr. Xiaofei Zhou
Dr. Yong Wu
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. Journal of Imaging 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

  • image and video understanding
  • AI-driven computer vision
  • AI-driven vision applications
  • multi-modal data processing
  • image and video quality assessment
  • image and video super-resolution
  • object segmentation and detection
  • saliency detection

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Published Papers (1 paper)

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Research

22 pages, 18974 KB  
Article
Lightweight 3D CNN for MRI Analysis in Alzheimer’s Disease: Balancing Accuracy and Efficiency
by Kerang Cao, Zhongqing Lu, Chengkui Zhao, Jiaming Du, Lele Li, Hoekyung Jung and Minghui Geng
J. Imaging 2025, 11(12), 426; https://doi.org/10.3390/jimaging11120426 - 28 Nov 2025
Viewed by 101
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by subtle structural changes in the brain, which can be observed through MRI scans. Although traditional diagnostic approaches rely on clinical and neuropsychological assessments, deep learning-based methods such as 3D convolutional neural networks (CNNs) [...] Read more.
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by subtle structural changes in the brain, which can be observed through MRI scans. Although traditional diagnostic approaches rely on clinical and neuropsychological assessments, deep learning-based methods such as 3D convolutional neural networks (CNNs) have recently been introduced to improve diagnostic accuracy. However, their high computational complexity remains a challenge. To address this, we propose a lightweight magnetic resonance imaging (MRI) classification framework that integrates adaptive multi-scale feature extraction with structural pruning and parameter optimization. The pruned model achieving a compact architecture with approximately 490k parameters (0.49 million), 4.39 billion floating-point operations, and a model size of 1.9 MB, while maintaining high classification performance across three binary tasks. The proposed framework was evaluated on the Alzheimer’s Disease Neuroimaging Initiative dataset, a widely used benchmark for AD research. Notably, the model achieves a performance density(PD) of 189.87, where PD is a custom efficiency metric defined as the classification accuracy per million parameters (% pm), which is approximately 70× higher than the basemodel, reflecting its balance between accuracy and computational efficiency. Experimental results demonstrate that the proposed framework significantly reduces resource consumption without compromising diagnostic performance, providing a practical foundation for real-time and resource-constrained clinical applications in Alzheimer’s disease detection. Full article
(This article belongs to the Special Issue AI-Driven Image and Video Understanding)
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