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Mathematics
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Application of Machine Learning and Mathematical Methods in Image Analysis...
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Application of Machine Learning and Mathematical Methods in Image Analysis and 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: 27 October 2026 | Viewed by 1170

Special Issue Editors

Dr. Hyun-Cheol Park

E-Mail Website
Guest Editor
Department of Computer Engineering, Korea National University of Transportation, 50, Daehak-ro, Daesowon-myeon, Chungju-si 27469, Republic of Korea
Interests: machine learning; deep learning; image analysis; computer vision; medical imaging
Dr. Dat Ngo

E-Mail Website
Guest Editor
Department of Computer Engineering, Korea National University of Transportation, Chungju 27469, Republic of Korea
Interests: digital image processing; machine learning; deep learning; biodata and bioimage analysis; brain-computer interface; FPGA prototyping; embedded system design; SoC design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, machine learning and optimization have become central tools in image analysis and computer vision, providing powerful methodologies for solving challenging real-world imaging problems. This Special Issue focuses on new mathematical models, learning frameworks, and optimization algorithms that advance the state of the art in imaging science. We invite contributions that address both theoretical and practical aspects, including novel learning-based approaches, efficient optimization strategies, and interdisciplinary applications. Topics of interest include, but are not limited to, data-driven image reconstruction, variational and optimization-driven imaging methods, deep architectures for visual understanding, trustworthy and explainable AI for imaging, and rigorous analysis of algorithms for large-scale visual data. We especially welcome works that connect solid mathematical theory with innovative applications in areas such as medical imaging, remote sensing, autonomous systems, and industrial inspection.

Dr. Hyun-Cheol Park
Dr. Dat Ngo
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. 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

  • machine learning
  • deep learning
  • optimization methods
  • image analysis
  • computer vision
  • variational imaging
  • inverse problems
  • medical imaging
  • remote sensing
  • pattern recognition

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Published Papers (2 papers)

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19 pages, 19686 KB  
Article
A Texture-Aware CNN Predictor for Reversible Data Hiding
by Mohsin Shah and Chang Choi
Mathematics 2026, 14(9), 1542; https://doi.org/10.3390/math14091542 - 1 May 2026
Viewed by 212
Abstract
Reversible data hiding (RDH) enables the reversible embedding of additional data into cover media, allowing the cover media to be perfectly recovered after extracting the embedded data. RDH relies on accurate prediction of pixels to generate sharply distributed prediction error histograms, thereby maximizing [...] Read more.
Reversible data hiding (RDH) enables the reversible embedding of additional data into cover media, allowing the cover media to be perfectly recovered after extracting the embedded data. RDH relies on accurate prediction of pixels to generate sharply distributed prediction error histograms, thereby maximizing embedding capacity and minimizing visual distortion. While convolutional neural network (CNN)-based predictors excel in smooth regions of cover images by leveraging local correlation, they often fail to produce accurate predictions in the textured regions. To address the limitation of CNN predictors, we propose a novel attention fusion-based CNN predictor (AFCNNP) that adaptively combines the CNN predictor with a non-local means (NLM) predictor. The proposed fusion framework learns spatial weight maps to favor CNN predictions in smooth regions and NLM predictions in textured regions. The experimental results show that the proposed framework outperforms other state-of-the-art CNN predictors by significantly lowering the mean absolute error, mean squared error, and variance of prediction errors, leading to more accurate pixel predictions. With the proposed fusion framework, the embedding and visual performance of prediction error expansion (PEE)-based RDH is improved compared to typical CNN-based RDH methods. Full article
(This article belongs to the Special Issue Application of Machine Learning and Mathematical Methods in Image Analysis and Computer Vision)
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20 pages, 11231 KB  
Article
YOLO-Based Shading Artifact Reduction for CBCT-to-MDCT Translation Using Two-Stage Learning
by Yangheon Lee and Hyun-Cheol Park
Mathematics 2026, 14(7), 1223; https://doi.org/10.3390/math14071223 - 6 Apr 2026
Viewed by 486
Abstract
Cone-beam computed tomography (CBCT) offers advantages of low radiation dose and rapid acquisition but suffers from scatter-induced shading artifacts that limit diagnostic value compared to multi-detector CT (MDCT). While CycleGAN enables unpaired image translation, its uniform loss application struggles with localized artifact removal. [...] Read more.
Cone-beam computed tomography (CBCT) offers advantages of low radiation dose and rapid acquisition but suffers from scatter-induced shading artifacts that limit diagnostic value compared to multi-detector CT (MDCT). While CycleGAN enables unpaired image translation, its uniform loss application struggles with localized artifact removal. We propose a two-stage learning framework with YOLO-based region correction loss. Stage 1 trains a standard CycleGAN to establish stable CBCT-MDCT domain mapping. Stage 2 fine-tunes the model by applying gradient magnitude minimization loss selectively to artifact regions detected by a pretrained YOLO detector, enabling focused correction while preserving anatomical structures. Using 11,000 2D CBCT slices from 17 patients (14 training, 3 testing) and 23,500 2D MDCT slices from 50 patients, our method achieves a 14.0% reduction in artifact score compared to baseline CycleGAN while maintaining high structural similarity (SSIM > 0.96). Independent evaluation using integral nonuniformity (INU) and shading index (SI) confirms consistent improvement across physics-based metrics. The self-regulating mechanism, where YOLO detection confidence naturally decreases as artifacts diminish, provides automatic adjustment without manual intervention. This work demonstrates that combining staged learning with object detection offers an effective solution for localized artifact removal in medical image translation, potentially improving diagnostic accuracy while preserving the low-dose benefits of CBCT. Full article
(This article belongs to the Special Issue Application of Machine Learning and Mathematical Methods in Image Analysis and Computer Vision)
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