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Mathematics
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Mathematical Techniques and Artificial Intelligence in Image Processing
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Mathematical Techniques and Artificial Intelligence in Image Processing

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 5312

Special Issue Editors

Dr. Juncheng Li

E-Mail Website
Guest Editor
School of Communication & Information Engineering, Shanghai University, Shanghai 200444, China
Interests: computer vision; medical image processing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Longguang Wang

E-Mail Website
Guest Editor
College of Electronic Science, Aviation University of Air Force, Changchun 130012, China
Interests: computer vision; image processing; 3D vision
Dr. Yingqian Wang

E-Mail Website
Guest Editor
College of Electronic Science and Technology, National University of Defense Technology (NUDT), Changsha 410072, China
Interests: computer vision; image processing; optical imaging and detection

Special Issue Information

Dear Colleagues,

The field of image processing has always been an essential component of the digital age, revolutionizing how we capture, manipulate, and understand visual data. In recent years, it has undergone a transformation due to the integration of mathematical techniques and artificial intelligence (AI). This transformation has not only improved the quality and efficiency of image processing tasks but has also opened a plethora of new possibilities in areas such as computer vision, medical imaging, and remote sensing.

Mathematical techniques have always played a fundamental role in image processing, from basic operations like convolution and filtering to more complex algorithms like wavelet transforms and Fourier analysis. These techniques provide the theoretical frameworks and computational tools necessary for manipulating and analyzing image data. On the other hand, AI algorithms, particularly machine learning and deep learning models, enable us to extract meaningful information from vast datasets in an efficient and automated manner.

Together, these two fields are paving the way for a new era of image processing, one that is more robust, versatile, and intelligent than ever before. This Special Issue provides a comprehensive overview of the latest advancements in the application of mathematical techniques and AI in image processing, including but not limited to mathematical methods and AI technologies in image recognition, object detection, image generation, and image enhancement. We invite you to submit your papers and participate in academic exchanges and collaborations in this field to jointly promote the development of image processing.

Dr. Juncheng Li
Dr. Longguang Wang
Dr. Yingqian Wang
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

  • artificial intelligence
  • image processing
  • computer vision
  • pattern recognition
  • deep learning
  • image recognition
  • image enhancement
  • image generation
  • image security

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Published Papers (4 papers)

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Research

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21 pages, 5849 KiB  
Article
MMCMOO: A Novel Multispectral Pansharpening Method
by Yingxia Chen and Yingying Xu
Mathematics 2024, 12(14), 2255; https://doi.org/10.3390/math12142255 - 19 Jul 2024
Viewed by 691
Abstract
From the perspective of optimization, most of the current mainstream remote sensing data fusion methods are based on traditional mathematical optimization or single objective optimization. The former requires manual parameter tuning and easily falls into local optimum. Although the latter can overcome the [...] Read more.
From the perspective of optimization, most of the current mainstream remote sensing data fusion methods are based on traditional mathematical optimization or single objective optimization. The former requires manual parameter tuning and easily falls into local optimum. Although the latter can overcome the shortcomings of traditional methods, the single optimization objective makes it unable to combine the advantages of multiple models, which may lead to distortion of the fused image. To address the problems of missing multi-model combination and parameters needing to be set manually in the existing methods, a pansharpening method based on multi-model collaboration and multi-objective optimization is proposed, called MMCMOO. In the proposed new method, the multi-spectral image fusion problem is transformed into a multi-objective optimization problem. Different evolutionary strategies are used to design a variety of population generation mechanisms, and a non-dominated sorting genetic algorithm (NSGA-II) is used to optimize the two proposed target models, so as to obtain the best pansharpening quality. The experimental results show that the proposed method is superior to the traditional methods and single objective methods in terms of visual comparison and quantitative analysis on our datasets. Full article
(This article belongs to the Special Issue Mathematical Techniques and Artificial Intelligence in Image Processing)
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16 pages, 11779 KiB  
Article
Research on the Automatic Detection of Ship Targets Based on an Improved YOLO v5 Algorithm and Model Optimization
by Xiaorui Sun, Henan Wu, Guang Yu and Nan Zheng
Mathematics 2024, 12(11), 1714; https://doi.org/10.3390/math12111714 - 30 May 2024
Cited by 2 | Viewed by 1022
Abstract
Because of the vast ocean area and the large amount of high-resolution image data, ship detection and data processing have become more difficult. These difficulties can be solved using the artificial intelligence interpretation method. The efficient and accurate detection ability of ship target [...] Read more.
Because of the vast ocean area and the large amount of high-resolution image data, ship detection and data processing have become more difficult. These difficulties can be solved using the artificial intelligence interpretation method. The efficient and accurate detection ability of ship target detection has been widely recognized with the increasing application of deep learning technology. It is widely used in the practice of ship target detection. Firstly, we set up a data set concerning ship targets by collecting and training a large number of images. Then, we improved the YOLO v5 algorithm. The feature specify module (FSM) is used in the improved algorithm. The improved YOLO v5 algorithm was applied to ship detection practice under the framework of Anaconda. Finally, the training results were optimized, and the false alarm rate was reduced. The detection rate was improved. According to the statistics pertaining to experimental results with other algorithm models, the improved YOLO v5 algorithm can effectively suppress conflicting information, and the detection ability of ship details is improved. This work has accumulated valuable experience for related follow-up research. Full article
(This article belongs to the Special Issue Mathematical Techniques and Artificial Intelligence in Image Processing)
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13 pages, 21597 KiB  
Article
SGNet: Efficient Snow Removal Deep Network with a Global Windowing Transformer
by Lie Shan, Haoxiang Zhang and Bodong Cheng
Mathematics 2024, 12(10), 1424; https://doi.org/10.3390/math12101424 - 7 May 2024
Cited by 1 | Viewed by 1267
Abstract
Image restoration under adverse weather conditions poses a challenging task. Previous research efforts have predominantly focused on eliminating rain and fog phenomena from images. However, snow, being another common atmospheric occurrence, also significantly impacts advanced computer vision tasks such as object detection and [...] Read more.
Image restoration under adverse weather conditions poses a challenging task. Previous research efforts have predominantly focused on eliminating rain and fog phenomena from images. However, snow, being another common atmospheric occurrence, also significantly impacts advanced computer vision tasks such as object detection and semantic segmentation. Recently, there has been a surge of methods specifically targeting snow removal, with the majority employing visual Transformers as the backbone network to enhance restoration effectiveness. Nevertheless, due to the quadratic computations required by Transformers to model long-range dependencies, this significantly escalates the time and space consumption of deep learning models. To address this issue, this paper proposes an efficient snow removal Transformer with a global windowing network (SGNet). This method forgoes the localized windowing strategy of previous visual Transformers, opting instead to partition the image into multiple low-resolution subimages containing global information using wavelet sampling, thereby ensuring higher performance while reducing computational overhead. Extensive experimentation demonstrates that our approach achieves outstanding performance across a wide range of benchmark datasets and can rival methods employing CNNs in terms of computational cost. Full article
(This article belongs to the Special Issue Mathematical Techniques and Artificial Intelligence in Image Processing)
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Review

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26 pages, 1164 KiB  
Review
Digital Watermarking Technology for AI-Generated Images: A Survey
by Huixin Luo, Li Li and Juncheng Li
Mathematics 2025, 13(4), 651; https://doi.org/10.3390/math13040651 - 16 Feb 2025
Viewed by 1217
Abstract
The rapid advancement of AI-generated content (AIGC) has significantly improved the realism and accessibility of synthetic images. While large image generation models offer immense potential in creative industries, they also introduce serious challenges, including copyright infringement, content authentication, and the traceability of generated [...] Read more.
The rapid advancement of AI-generated content (AIGC) has significantly improved the realism and accessibility of synthetic images. While large image generation models offer immense potential in creative industries, they also introduce serious challenges, including copyright infringement, content authentication, and the traceability of generated images. Digital watermarking has emerged as a promising approach to address these concerns by embedding imperceptible yet detectable signatures into generated images. This survey provides a comprehensive review of three core areas: (1) the evolution of image generation technologies, highlighting key milestones such as the transition from GANs to diffusion models; (2) traditional and state-of-the-art digital image watermarking algorithms, encompassing spatial domain, transform domain, and deep learning-based approaches; (3) watermarking methods specific to AIGC, including ownership authentication of AI model and diffusion model, and watermarking of AI-generated images. Additionally, we examine common performance evaluation metrics used in this field, such as watermark capacity, watermark detection accuracy, fidelity, and robustness. Finally, we discuss the unresolved issues and propose several potential directions for future research. We look forward to this paper offering valuable reference for academics in the field of AIGC watermarking and related fields. Full article
(This article belongs to the Special Issue Mathematical Techniques and Artificial Intelligence in Image Processing)
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