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Information
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Intelligent Image Processing by Deep Learning, 2nd Edition
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Intelligent Image Processing by Deep Learning, 2nd Edition

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

Deadline for manuscript submissions: 6 February 2026 | Viewed by 268

Special Issue Editors

Prof. Dr. Dongming Zhou

E-Mail Website
Guest Editor
School of Information Science & Engineering, Yunnan University, Kunming 650000, China
Interests: Artificial Intelligence; pattern recognition; image processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Haiyan Li

E-Mail Website
Guest Editor
School of Information Science & Engineering, Yunnan University, Kunming 650000, China
Interests: Artificial Intelligence; pattern recognition; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Image processing plays a crucial role in various domains, including computer vision, information analysis, and multimedia applications. With the advent of deep learning and artificial intelligence techniques, there is a growing interest in developing intelligent image processing systems that can effectively analyze, enhance, and interpret images. However, challenges like, dataset limitations, interpretability and explainability, computational complexity, and many other issues still exit that need to be addressed in order to achieve further progress and widespread adoption. This Special Issue aims to provide a platform for researchers and practitioners to explore the advancements and applications of deep learning-based approaches in the field of image processing. Authors are encouraged to present novel deep learning-based methodologies, algorithms, and frameworks that contribute to the advancement of intelligent image processing. Submissions may include theoretical contributions, experimental evaluations, and practical applications. We welcome original research papers, survey articles, and systematic literature reviews that address the challenges and opportunities in the field of intelligent image processing using deep learning. Manuscripts should demonstrate the effectiveness, efficiency, and applicability of the proposed methods. We invite original research articles, case studies, and reviews that address related topics, such as the following:

  1. Image inpainting and restoration using deep learning techniques.
  2. Advanced image analysis and understanding.
  3. Information processing and analysis in images.
  4. Computer vision algorithms and applications.
  5. Machine learning for image processing and analysis.
  6. Intelligent image processing system design and implementation.
  7. Multimodal target monitoring and tracking techniques.
  8. Multimodal image fusion and enhancement approaches.
  9. Application and case studies of deep learning in image processing.

Prof. Dr. Dongming Zhou
Prof. Dr. Haiyan Li
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. 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 1600 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
  • image inpainting
  • image analysis
  • information processing
  • information analysis
  • computer vision
  • deep learning
  • machine learning
  • application and case studies
  • intelligent image processing system
  • multimodal target monitoring and tracking
  • multimodal image fusion
  • Artificial Intelligence

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Published Papers (1 paper)

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Research

19 pages, 17487 KiB  
Article
LiteMP-VTON: A Knowledge-Distilled Diffusion Model for Realistic and Efficient Virtual Try-On
by Shufang Zhang, Lei Wang and Wenxin Ding
Information 2025, 16(5), 408; https://doi.org/10.3390/info16050408 - 15 May 2025
Viewed by 88
Abstract
Diffusion-based approaches have recently emerged as powerful alternatives to GAN-based virtual try-on methods, offering improved detail preservation and visual realism. Despite their advantages, the substantial number of parameters and intensive computational requirements pose significant barriers to deployment on low-resource platforms. To tackle these [...] Read more.
Diffusion-based approaches have recently emerged as powerful alternatives to GAN-based virtual try-on methods, offering improved detail preservation and visual realism. Despite their advantages, the substantial number of parameters and intensive computational requirements pose significant barriers to deployment on low-resource platforms. To tackle these limitations, we propose a diffusion-based virtual try-on framework optimized through feature-level knowledge compression. Our method introduces MP-VTON, an enhanced inpainting pipeline based on Stable Diffusion, which incorporates improved Masking techniques and Pose-conditioned enhancement to alleviate garment boundary artifacts. To reduce model size while maintaining performance, we adopt an attention-guided distillation strategy that transfers semantic and structural knowledge from MP-VTON to a lightweight model, LiteMP-VTON. Experiments demonstrate that LiteMP-VTON achieves nearly a 3× reduction in parameter count and close to 2× speedup in inference, making it well suited for deployment in resource-limited environments without significantly compromising generation quality. Full article
(This article belongs to the Special Issue Intelligent Image Processing by Deep Learning, 2nd Edition)
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