applsci-logo

Journal Browser

Journal Browser

Digital Image Processing: Novel Technologies and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 April 2025) | Viewed by 1627

Special Issue Editors


E-Mail Website
Guest Editor
Faculty of Informatics, Juraj Dobrila University of Pula, Pula, Croatia
Interests: object recognition; image processing; artificial intelligence

E-Mail Website
Guest Editor
Faculty of Informatics, Juraj Dobrila University of Pula, Pula, Croatia
Interests: distributed systems; machine learning; cloud computing

Special Issue Information

Dear Colleagues,

In today's era, digital image processing stands as a cornerstone technology with profound implications across various domains. Its applications span from surveillance systems to medical diagnostics, revolutionizing the way we interact with visual data. However, despite significant advancements, there remain unexplored avenues and emerging technologies that promise to redefine the landscape of digital image processing. This Special Issue aims to showcase cutting-edge research and innovative applications in digital image processing, providing a platform for the dissemination of novel methodologies, algorithms, and tools. Additionally, we invite contributions that introduce new datasets and foster collaboration in this dynamic field, accelerating its progress and impact on diverse domains.
This Special Issue is dedicated to exploring the latest advancements in digital image processing, presenting a platform for innovative research and practical applications.
The following are some of the key research interests we invite contributions on:

  • Deep learning approaches for image classification and recognition;
  • Image segmentation techniques for medical imaging analysis;
  • Enhanced image restoration algorithms for degraded visual data;
  • Multi-modal image fusion methods for improved information extraction;
  • Real-time image processing systems and architectures;
  • Image-based biometric authentication and security applications;
  • Semantic image understanding and scene understanding;
  • Adaptive image filtering and noise reduction strategies;
  • Augmented reality and virtual reality applications leveraging image processing techniques;
  • Ethical considerations and societal impacts of digital image processing technologies;
  • Edge computing solutions for efficient image processing in distributed systems;
  • Collaborative image processing in peer-to-peer architectures;
  • Privacy-preserving techniques in digital image processing;
  • Generative methods for synthetic data curation.

We encourage researchers and practitioners to submit their work on these topics as well as others to foster discussions and advancements in the field of digital image processing.

Dr. Ivan Lorencin
Dr. Nikola Tanković
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. Applied Sciences 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 2400 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

  • digital image processing
  • deep learning
  • image segmentation
  • collaborative image processing
  • augmented reality
  • virtual reality

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

14 pages, 26445 KiB  
Article
Containment Control-Guided Boundary Information for Semantic Segmentation
by Wenbo Liu, Junfeng Zhang, Chunyu Zhao, Yi Huang, Tao Deng and Fei Yan
Appl. Sci. 2024, 14(16), 7291; https://doi.org/10.3390/app14167291 - 19 Aug 2024
Viewed by 1137
Abstract
Real-time semantic segmentation is a challenging task in computer vision, especially in complex scenes. In this study, a novel three-branch semantic segmentation model is designed, aiming to effectively use boundary information to improve the accuracy of semantic segmentation. The proposed model introduces the [...] Read more.
Real-time semantic segmentation is a challenging task in computer vision, especially in complex scenes. In this study, a novel three-branch semantic segmentation model is designed, aiming to effectively use boundary information to improve the accuracy of semantic segmentation. The proposed model introduces the concept of containment control in a pioneering way, which treats image interior elements as well as image boundary elements as followers and leaders in containment control, respectively. Based on this, we utilize two learnable feature fusion matrices in the high-level semantic information stage of the model to quantify the fusion process of internal and boundary features. Further, we design a dedicated loss function to update the parameters of the feature fusion matrices based on the criterion of containment control, which enables fine-grained communication between target features. In addition, our model incorporates a Feature Enhancement Unit (FEU) to tackle the challenge of maximizing the utility of multi-scale features essential for semantic segmentation tasks through the meticulous reconstruction of these features. The proposed model proves effective on the publicly available Cityscapes and CamVid datasets, achieving a trade-off between effectiveness and speed. Full article
(This article belongs to the Special Issue Digital Image Processing: Novel Technologies and Applications)
Show Figures

Figure 1

Back to TopTop