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Symmetry
Special Issues
Symmetry-Aware Methods in Image Processing and Computer Vision
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Symmetry-Aware Methods in Image Processing and Computer Vision

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2005

Special Issue Editors

Prof. Dr. Nabil El Akkad

E-Mail Website
Guest Editor
National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco
Interests: computer vision; image processing; AI
Prof. Dr. Mostafa Merras

E-Mail Website
Guest Editor
IEVIA Team, IMAGE Laboratory, ESTM, Moulay Ismail University of Meknes, Toulal B.P. 3103, Morocco
Interests: computer vision; security; AI; ML

Special Issue Information

Dear Colleagues,

Recent advances in image processing and computer vision, driven by improved image acquisition and intelligent devices, have enabled wide real-world applications and rapid progress in symmetry-aware techniques. By exploiting geometric symmetry and invariance, modern methods achieve improved efficiency, robustness, and accuracy in visual analysis.

Deep learning, particularly symmetry-aware neural networks, has significantly advanced image processing and computer vision by leveraging large-scale data. However, these models often require substantial computational resources, limiting their deployment in real-time and resource-constrained environments. To overcome these challenges, lightweight and efficient network architectures that incorporate symmetry have gained increasing attention.

This Special Issue focuses on recent developments in symmetry-aware and lightweight methods for fast image processing and computer vision. We welcome original research contributions on topics including, but not limited to, the following:

  • Symmetry-aware and lightweight deep neural networks.
  • Fast image restoration, including denoising, super-resolution, and deblurring.
  • Efficient object detection and image segmentation.
  • Fast and symmetry-aware image style transfer.
  • Secure and privacy-preserving image processing using cryptography.
  • Homomorphic encryption and secure deep learning for images.
  • Image authentication, watermarking, and tamper detection for security.
  • Real-time secure image and video processing on resource-constrained devices.

We look forward to receiving your contributions and to fostering further advances in symmetry-aware image processing and computer vision.

Prof. Dr. Nabil El Akkad
Prof. Dr. Mostafa Merras
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. 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

  • symmetry-aware methods
  • image processing
  • deep learning
  • lightweight neural networks
  • super-resolution
  • image denoising

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

Published Papers (1 paper)

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26 pages, 7095 KB  
Article
CB-DETR: Symmetry-Guided Density-Adaptive Attention and Posterior Dynamic Query Decoding for Remote Sensing Target Detection
by Xiaodong Zhang, Jiahui Xue and Shengye Zhao
Symmetry 2026, 18(4), 561; https://doi.org/10.3390/sym18040561 - 25 Mar 2026
Viewed by 544
Abstract
Remote sensing object detection is severely hindered by background clutter and uneven object spatial distribution, limiting the performance of traditional algorithms and the original RT-DETR. To address these issues, this paper proposes an improved RT-DETR-based algorithm, CB-DETR. First, a symmetry-guided Density-Adaptive Attention (DAA) [...] Read more.
Remote sensing object detection is severely hindered by background clutter and uneven object spatial distribution, limiting the performance of traditional algorithms and the original RT-DETR. To address these issues, this paper proposes an improved RT-DETR-based algorithm, CB-DETR. First, a symmetry-guided Density-Adaptive Attention (DAA) module is designed to tackle insufficient intra-scale feature interaction and poor adaptability to uneven density regions in RT-DETR. Centered on a density estimation network, it predicts target density, generates normalized weights via temperature scaling and softmax, and dynamically adjusts receptive fields through a multi-branch structure to symmetrically adapt to high- and low-density regions, outperforming RT-DETR’s fixed receptive field design. Second, a cross-attention-fused Posterior Dynamic Query Decoder (PDQD) is constructed to overcome fixed query interaction and weak small/occluded object detection in the original decoder. A dynamic query update mechanism optimizes vectors via multi-round iterations, breaking fixed-layer limitations and mining detailed features in complex scenarios, thus improving small/occluded target detection accuracy. Comparative experiments on RSOD, DIOR, and DOTA datasets show that CB-DETR outperforms the original RT-DETR comprehensively: mAP50/mAP50:95 improve by 2.8%/2.1% and Precision (P)/Recall (R) by 4%/2.4% on RSOD; mAP50 improves by 1.3% on DIOR and 3% on DOTA. All core metrics surpass the original model and mainstream improved algorithms, verifying the effectiveness and innovation of the proposed improvements. Full article
(This article belongs to the Special Issue Symmetry-Aware Methods in Image Processing and Computer Vision)
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