Symmetry/Asymmetry in Digital Image Processing

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 16 May 2026 | Viewed by 353

Special Issue Editors

School of Physical Science and Technology, Northwestern Polytechnical University, Xi’an, China
Interests: statistical data analysis; image recognition and classification based on deep learning and neutral network; applications of deep neural network in physics neural network in physics
Special Issues, Collections and Topics in MDPI journals
School of Electronic Engineering, Xidian University, Xi’an, China
Interests: deep learning; video and image target detection; remote sensing image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Digital image processing is a multidisciplinary field rooted in computer science, mathematics, and engineering. It involves the analysis, enhancement, and interpretation of visual data through algorithms and computational techniques. Emerging from foundational work in signal processing and Fourier analysis in the mid-20th century, the field gained momentum with advances in computing power and the development of key theories like wavelet transforms and machine learning. This discipline is critically important across industries. In medicine, it enables early disease detection through MRI and CT scan analysis. Satellite imaging relies on it for environmental monitoring and urban planning, while autonomous vehicles use real-time image processing for navigation. The rise of deep learning has further expanded its applications, making it indispensable for facial recognition, industrial quality control, and even artistic creation. As visual data dominates the digital age, research in this domain directly drives technological innovation and societal progress.

Digital image processing also involves both symmetric and asymmetric phenomena that influence algorithm design and outcomes. The interplay between symmetry and asymmetry drives advancements in convolutional neural networks, medical imaging diagnostics, etc. We are pleased to invite you to make contributions to the area of digital image processing. This Special Issue aims to introduce the development of digital image processing using the techniques of deep neural networks and artificial intelligence. In this Special Issue, original research articles and reviews are welcome. Research topics may include (but are not limited to) the following:

  1. Digital image processing in medical diagnostics;
  2. Digital image processing in remote sensing;
  3. Target recognition and classification based on digital images;
  4. Computational digital image generation;
  5. Algorithms and computational techniques of digital image processing;
  6. Applications of digital image processing to other relevant areas, such as facial recognition, industrial quality control, and environmental detection.

We look forward to receiving your contributions.

Dr. Qiang Li
Dr. Wei Feng
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. Symmetry is an international peer-reviewed open access monthly 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
  • target recognition and classification
  • image generation
  • deep neural networks
  • artificial intelligence
  • remote sensing
  • deep learning

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Published Papers (1 paper)

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Research

23 pages, 5348 KB  
Article
A Symmetry-Aware Multi-Attention Framework for Bird Nest Detection on Railway Catenary Systems
by Peiting Shan, Wei Feng, Shuntian Lou, Gabriel Dauphin and Wenxing Bao
Symmetry 2025, 17(9), 1505; https://doi.org/10.3390/sym17091505 - 10 Sep 2025
Viewed by 212
Abstract
Railway service interruptions and electrical hazards often arise due to bird nests concealed within the intricate, highly symmetric overhead catenary networks of high-speed lines. These nests are difficult to pinpoint automatically, not only because they are diminutive and often merge visually with the [...] Read more.
Railway service interruptions and electrical hazards often arise due to bird nests concealed within the intricate, highly symmetric overhead catenary networks of high-speed lines. These nests are difficult to pinpoint automatically, not only because they are diminutive and often merge visually with the surroundings but also due to occlusions and the persistent lack of substantial labeled datasets. To address this bottleneck, this work presents the High-Speed Railway Catenary Nest Dataset (HRC-Nest), merging 800 authentic images and 1000 synthetic samples to capture a spectrum of scenarios. Building on the symmetry of catenary structures—where nests appear as localized asymmetries—the Symmetry-Aware Railway Nest Detection Framework (RNDF) is proposed, an enhanced YOLOv12 system for accurate and robust nest detection in symmetric high-speed railway catenary environments. With the A2C2f_HRAMi design, the RNDF learns from multi-level features by unifying residual and hierarchical attention strategies. The SCSA component boosts the recognition in visually cluttered or obstructed settings further by jointly processing spatial and channel-wise signals. To sharpen the detection accuracy, particularly for subtle, hidden nests, the Focaler-GIoU loss guides bounding box optimization. Comparative studies show that the RNDF consistently outperforms recent detectors, surpassing the YOLOv12 baseline by 5.95% mAP@0.5 and 26.16% mAP@0.5:0.95, underscoring its suitability for symmetry-aware, real-world catenary anomaly monitoring. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Digital Image Processing)
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