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Mathematics
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Intelligent Computing with Applications in Computer Vision
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Intelligent Computing with Applications in Computer Vision

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 1576

Special Issue Editors

Dr. Lei Yang

E-Mail Website
Guest Editor
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: robot vision; intelligent detection; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. En Li

E-Mail Website
Guest Editor
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Interests: robotics control; embedded system; sensor technology
Special Issues, Collections and Topics in MDPI journals
Dr. Fangyuan Li

E-Mail Website
Guest Editor
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: robotics control; multi-agent system; distributed optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last few years, there has been remarkable growth in the effectiveness and influence of computer vision technologies across a multitude of disciplines including biomedical imaging, industrial automation, and healthcare informatics, among others.  However, there are still many significant problems encountered in the research and application of computer vision technology and artificial intelligence. In light of these developments, in this Special Issue, we will compile state-of-the-art research and applied innovations within the realm of computational intelligence, with a particular emphasis on its intersections with image processing and computer vision.  

This collection will delve into a variety of themes, extending beyond the basic to encompass applications of image processing and computer vision methodologies. Areas of interest include (but are not restricted to) the following:

  • Image processing
  • Neural networks
  • Deep learning for efficient detection and segmentation
  • Deep learning for medical image analysis
  • Industrial applications of AI
  • Expert systems integrated with image processing and computer vision
  • Multi-modal image analysis
  • Deep learning theory and applications
  • Supervised/semi-supervised/unsupervised learning
  • Emerging trends in explainable AI (XAI)

We also invite authors to submit extended versions of their conference papers to this Special Issue. All papers accepted in this Special Issue will meet our usual high standards for publication in Mathematics.

Dr. Lei Yang
Prof. Dr. En Li
Dr. Fangyuan 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. Mathematics 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 2600 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
  • neural networks
  • machine learning
  • deep learning
  • artificial intelligence
  • computational intelligence

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Published Papers (2 papers)

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Research

26 pages, 11273 KiB  
Article
DREFNet: Deep Residual Enhanced Feature GAN for VVC Compressed Video Quality Improvement
by Tanni Das and Kiho Choi
Mathematics 2025, 13(10), 1609; https://doi.org/10.3390/math13101609 - 14 May 2025
Viewed by 151
Abstract
In recent years, the use of video content has experienced exponential growth. The rapid growth of video content has led to an increased reliance on various video codecs for efficient compression and transmission. However, several challenges are associated with codecs such as H.265/High [...] Read more.
In recent years, the use of video content has experienced exponential growth. The rapid growth of video content has led to an increased reliance on various video codecs for efficient compression and transmission. However, several challenges are associated with codecs such as H.265/High Efficiency Video Coding and H.266/Versatile Video Coding (VVC) that can impact video quality and performance. One significant challenge is the trade-off between compression efficiency and visual quality. While advanced codecs can significantly reduce file sizes, they introduce artifacts such as blocking, blurring, and color distortion, particularly in high-motion scenes. Different compression tools in modern video codecs are vital for minimizing artifacts that arise during the encoding and decoding processes. While the advanced algorithms used by these modern codecs can effectively decrease file sizes and enhance compression efficiency, they frequently find it challenging to eliminate artifacts entirely. By utilizing advanced techniques such as post-processing after the initial decoding, this method can significantly improve visual clarity and restore details that may have been compromised during compression. In this paper, we introduce a Deep Residual Enhanced Feature Generative Adversarial Network as a post-processing method aimed at further improving the quality of reconstructed frames from the advanced codec VVC. By utilizing the benefits of Deep Residual Blocks and Enhanced Feature Blocks, the generator network aims to make the reconstructed frame as similar as possible to the original frame. The discriminator network, a crucial element of our proposed method, plays a vital role in guiding the generator by evaluating the authenticity of generated frames. By distinguishing between fake and original frames, the discriminator enables the generator to improve the quality of its output. This feedback mechanism ensures that the generator learns to create more realistic frames, ultimately enhancing the overall performance of the model. The proposed method shows significant gain for Random Access (RA) and All Intra (AI) configurations while improving Video Multimethod Assessment Fusion (VMAF) and Multi-Scale Structural Similarity Index Measure (MS-SSIM). Considering VMAF, our proposed method can obtain 13.05% and 11.09% Bjøntegaard Delta Rate (BD-Rate) gain for RA and AI configuration, respectively. In the case of the luma component MS-SSIM, RA and AI configurations get, respectively, 5.00% and 5.87% BD-Rate gain after employing our suggested proposed network. Full article
(This article belongs to the Special Issue Intelligent Computing with Applications in Computer Vision)
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17 pages, 21415 KiB  
Article
A Novel Method for Localized Typical Blemish Image Data Generation in Substations
by Na Zhang, Jingjing Fan, Gang Yang, Guodong Li, Hong Yang and Yang Bai
Mathematics 2024, 12(18), 2950; https://doi.org/10.3390/math12182950 - 23 Sep 2024
Viewed by 903
Abstract
Current mainstream methods for detecting surface blemishes on substation equipment typically rely on extensive sets of blemish images for training. However, the unpredictable nature and infrequent occurrence of such blemishes present significant challenges in data collection. To tackle these issues, this paper proposes [...] Read more.
Current mainstream methods for detecting surface blemishes on substation equipment typically rely on extensive sets of blemish images for training. However, the unpredictable nature and infrequent occurrence of such blemishes present significant challenges in data collection. To tackle these issues, this paper proposes a novel approach for generating localized, representative blemish images within substations. Firstly, to mitigate global style variations in images generated by generative adversarial networks (GANs), we developed a YOLO-LRD method focusing on local region detection within equipment. This method enables precise identification of blemish locations in substation equipment images. Secondly, we introduce a SEB-GAN model tailored specifically for generating blemish images within substations. By confining blemish generation to identified regions within equipment images, the authenticity and diversity of the generated defect data are significantly enhanced. Theexperimental results validate that the YOLO-LRD and SEB-GAN techniques effectively create precise datasets depicting flaws in substations. Full article
(This article belongs to the Special Issue Intelligent Computing with Applications in Computer Vision)
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