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Mathematics
Special Issues
Mathematical Innovations and Contributions within Communication and Information Processing
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Mathematical Innovations and Contributions within Communication and Information Processing

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 781

Special Issue Editors

Prof. Dr. Li Guo

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Guest Editor
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: digital signal processing; blockchain technology; intelligent medical image processing; precision medical big data analysis
Prof. Dr. Xinchao Zhao

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Guest Editor
School of Science, Beijing University of Posts and Telecommunications, Beijing, China
Interests: swarm intelligence; operations research
Special Issues, Collections and Topics in MDPI journals
Dr. Jesus Requena-Carrión

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Guest Editor
School of Electronics Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
Interests: statistical data and signal processing; modeling and simulation; biomedical engineering; machine learning project management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xingquan Zuo

E-Mail Website
Guest Editor
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: intelligent optimization; data mining; artificial intelligence; intelligent transportation systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The primary objective of this Special Issue aligns with the proceedings of the ‘2023 9th International Conference on Communication and Information Processing (ICCIP 2023),’ a pivotal event aimed at addressing unprecedented advancements in the mathematical aspects of Communication and Information Processing: Theories and Applications. Held in Lingshui, Hainan, China, in December 14–16, 2023, the conference's website, http://www.iccip.org/, serves as a repository of invaluable information.

ICCIP 2023 is dedicated to exploring cutting-edge theories and mathematical applications within communication and information processing technologies, intending to revolutionize future developments.

Featuring world-class plenary speakers, technical symposiums, and specialized tracks, ICCIP 2023 invites original mathematical papers for inclusion in its proceedings. This international conference provides a pivotal platform for researchers to converge and explore mathematical complexities across diverse areas within this field.

Moreover, this Special Issue warmly invites manuscripts that complement the conference's themes and keywords, specifically focusing on mathematical aspects not previously presented at ICCIP 2023. Such contributions are encouraged to further the mathematical underpinnings of Communication and Information Processing: Theories and Applications.

Prof. Dr. Li Guo
Prof. Dr. Xinchao Zhao
Dr. Jesus Requena-Carrión
Prof. Dr. Xingquan Zuo
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

  • block chain and cryptography
  • cloud computing and scheduling optimization
  • computational intelligence and grid computing
  • computer crime prevention and detection
  • computer security
  • data mining and big data analysis
  • data stream processing in mobile/sensor networks
  • distributed and parallel applications
  • evolutionary learning and optimization
  • fuzzy and neural network systems
  • image processing and signal processing
  • information and data management
  • information and network security
  • mobile and social network programming
  • multimedia computing
  • mathematical optimization and swarm intelligence
  • software engineering
  • ubiquitous computing, services and applications
  • web services modeling and web composition
  • wireless communications optimization

Published Papers (1 paper)

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Research

18 pages, 2822 KiB  
Article
Learning the Hybrid Nonlocal Self-Similarity Prior for Image Restoration
by Wei Yuan, Han Liu, Lili Liang and Wenqing Wang
Mathematics 2024, 12(9), 1412; https://0-doi-org.brum.beds.ac.uk/10.3390/math12091412 - 6 May 2024
Viewed by 379
Abstract
As an immensely important characteristic of natural images, the nonlocal self-similarity (NSS) prior has demonstrated great promise in a variety of inverse problems. Unfortunately, most current methods utilize either the internal or the external NSS prior learned from the degraded image or training [...] Read more.
As an immensely important characteristic of natural images, the nonlocal self-similarity (NSS) prior has demonstrated great promise in a variety of inverse problems. Unfortunately, most current methods utilize either the internal or the external NSS prior learned from the degraded image or training images. The former is inevitably disturbed by degradation, while the latter is not adapted to the image to be restored. To mitigate such problems, this work proposes to learn a hybrid NSS prior from both internal images and external training images and employs it in image restoration tasks. To achieve our aims, we first learn internal and external NSS priors from the measured image and high-quality image sets, respectively. Then, with the learned priors, an efficient method, involving only singular value decomposition (SVD) and a simple weighting method, is developed to learn the HNSS prior for patch groups. Subsequently, taking the learned HNSS prior as the dictionary, we formulate a structural sparse representation model with adaptive regularization parameters called HNSS-SSR for image restoration, and a general and efficient image restoration algorithm is developed via an alternating minimization strategy. The experimental results indicate that the proposed HNSS-SSR-based restoration method exceeds many existing competition algorithms in PSNR and SSIM values. Full article
(This article belongs to the Special Issue Mathematical Innovations and Contributions within Communication and Information Processing)
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