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Symmetry/Asymmetry in Image Processing and Computer Vision

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A Special Issue of Symmetry (ISSN 2073-8994) belonging to the section "A: Computer Science".

Deadline for manuscript submissions: closed (31 August 2026) | Viewed by 18499

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Editors

Dr. Xingbo Dong

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Guest Editor

Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, School of Artificial Intelligence, Anhui University, Hefei 230601, China
Interests: image processing (both medical and natural); computer vision; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Dr. Shuo Yang

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Guest Editor

College of Computer Science, Inner Mongolia University, Hohhot 010031, China
Interests: computer vision; image processing; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of Symmetry focuses on the exploration of symmetry and asymmetry in image processing and computer vision, aiming to advance the understanding of these fundamental principles and their applications in modern visual technologies. Symmetry and asymmetry play crucial roles in a wide range of computer vision tasks, including object recognition, image segmentation, feature extraction, and 3D reconstruction. By investigating the impact of symmetry and asymmetry on algorithm design and performance, contributors can offer insights into how these principles can be harnessed to enhance image quality, improve computational efficiency, and achieve robust results in complex visual environments.

We invite contributions that delve into the theoretical and practical aspects of symmetry and asymmetry in image processing and computer vision, as well as innovative methods for leveraging these principles. Topics may include, but are not limited to, symmetry-based feature extraction, deep learning architectures, asymmetry handling in face and object recognition, applications of symmetry and asymmetry in medical imaging, and challenges in processing asymmetric or imperfectly symmetric data.

This Special Issue aims to serve as a collaborative platform for researchers, developers, and industry professionals, fostering the exchange of new ideas, techniques, and best practices. We hope to create a comprehensive resource that demonstrates the versatility of symmetry and asymmetry in solving critical challenges in image processing and computer vision, ultimately advancing the field and inspiring future developments.

Dr. Xingbo Dong
Dr. Shuo Yang
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-anonymized 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

image processing
computer vision
pattern recognition
object detection
image segmentation
deep learning
machine learning
convolutional neural network

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Symmetry/Asymmetry in Image Processing and Computer Vision, 2nd Edition in Symmetry

Published Papers (10 papers)

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Research

22 pages, 10915 KB

Open AccessArticle

Symmetry-Aware Progressive Generative Modeling for Non-Invasive Digital Restoration of Dunhuang Murals

by Ping Wen, Feng Ao, Xilin Liu and Zhongbin Luo

Symmetry 2026, 18(8), 1278; https://doi.org/10.3390/sym18081278 - 28 Jul 2026

Viewed by 356

Abstract

Symmetry and asymmetry play an important role in image processing and computer vision, particularly when visual structures are corrupted by irregular and spatially heterogeneous degradation. In cultural heritage restoration, ancient murals often contain locally symmetric patterns, repeated decorative motifs, balanced compositions, and style-sensitive contours, while long-term aging introduces asymmetric damage such as cracks, pigment fading, flaking, and partial content loss. Restoring such images therefore requires models that can recover structural regularity from asymmetric degradation while preserving culturally meaningful visual details. In this paper, we propose a symmetry-aware progressive generative framework for non-invasive digital restoration of Dunhuang murals. The proposed model is implemented as a Cauchy–Schwarz-regularized cascading variational autoencoder, which decomposes restoration into three coarse-to-fine stages: global structural recovery, semantic and chromatic refinement, and fine-detail enhancement. To support this progressive process, the latent dimensionality is gradually expanded across stages, enabling the model to move from compact structural abstraction to detail-aware representation learning. Moreover, a Cauchy–Schwarz-divergence-based regularization strategy is introduced to align the aggregated posterior with a mixture-of-Gaussians prior, providing a tractable mechanism for modeling the multi-modal latent structure of mural images. Experiments on the MuralDH benchmark under irregular-mask, crack-like, and mixed degradation settings show that the proposed method achieves competitive restoration quality compared with representative inpainting and diffusion-based baselines, while requiring substantially lower inference cost. Qualitative results further demonstrate improved contour continuity, chromatic coherence, and texture preservation. These results suggest that symmetry-aware progressive generative modeling is a promising tool for sustainable, non-invasive cultural heritage restoration. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry in Image Processing and Computer Vision)

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32 pages, 12128 KB

Open AccessArticle

YOLO-SMD: A Symmetrical Multi-Scale Feature Modulation Framework for Pediatric Pneumonia Detection

by Linping Du, Xiaoli Zhu, Zhongbin Luo and Yanping Xu

Symmetry 2026, 18(1), 139; https://doi.org/10.3390/sym18010139 - 10 Jan 2026

Viewed by 726

Abstract

Pediatric pneumonia detection faces the challenge of pathological asymmetry, where immature lung tissues present blurred boundaries and lesions exhibit extreme scale variations (e.g., small viral nodules vs. large bacterial consolidations). Conventional detectors often fail to address these imbalances. In this study, we propose YOLO-SMD, a detection framework built upon a symmetrical design philosophy to enforce balanced feature representation. We introduce three architectural innovations: (1) DySample (Content-Aware Upsampling): To address the blurred boundaries of pediatric lesions, this module replaces static interpolation with dynamic point sampling, effectively sharpening edge details that are typically smoothed out by standard upsamplers; (2) SAC2f (Cross-Dimensional Attention): To counteract background interference, this module enforces a symmetrical interaction between spatial and channel dimensions, allowing the model to suppress structural noise (e.g., rib overlaps) in low-contrast X-rays; (3) SDFM (Adaptive Gated Fusion): To resolve the extreme scale disparity, this unit employs a gated mechanism that symmetrically balances deep semantic features (crucial for large bacterial shapes) and shallow textural features (crucial for viral textures). Extensive experiments on a curated subset of 2611 images derived from the Chest X-ray Pneumonia Dataset demonstrate that YOLO-SMD achieves competitive performance with a focus on high sensitivity, attaining a Recall of 86.1% and an mAP@0.5 of 84.3%, thereby outperforming the state-of-the-art YOLOv12n by 2.4% in Recall under identical experimental conditions. The results validate that incorporating symmetry principles into feature modulation significantly enhances detection robustness in primary healthcare settings. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry in Image Processing and Computer Vision)

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19 pages, 5679 KB

Open AccessArticle

SDDNet: Two-Stage Network for Forgings Surface Defect Detection

by Shentao Wang, Depeng Gao, Byung-Won Min, Yue Hong, Tingting Xu and Zhongyue Xiong

Symmetry 2026, 18(1), 104; https://doi.org/10.3390/sym18010104 - 6 Jan 2026

Viewed by 799

Abstract

Detecting surface defects in forgings is crucial for ensuring the reliability of automotive components such as steering knuckles. In fluorescent magnetic particle inspection (FDMPI) images, normal forging surfaces generally exhibit locally symmetric texture patterns, whereas cracks and other flaws appear as locally asymmetric regions. Traditional FDMPI inspection relies on manual visual judgement, which is inefficient and error-prone. This paper introduces SDDNet, a symmetry-aware deep learning model for surface defect detection in FDMPI images. A dedicated FDMPI dataset is constructed and further expanded using a denoising diffusion probabilistic model (DDPM) to improve training robustness. To better separate symmetric background textures from asymmetric defect cues, SDDNet integrates a UPerNet-based segmentation layer for background suppression and a Scale-Variant Inception Module (SVIM) within an RTMDet framework for multi-scale feature extraction. Experiments show that SDDNet effectively suppresses background noise and significantly improves detection accuracy, achieving a mean average precision (mAP) of 45.5% on the FDMPI dataset, 19% higher than the baseline, and 71.5% mAP on the NEU-DET dataset, outperforming existing methods by up to 8.1%. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry in Image Processing and Computer Vision)

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24 pages, 11779 KB

Open AccessArticle

Aircraft Trajectory Tracking via Geometric Prior-Guided Keypoint Detection in SMR

by Xiaoyan Wang, Jiangyan Ji, Mingmin Wu, Peng Li, Xiangli Wang, Zhaowen Tong and Zhixiang Huang

Symmetry 2025, 17(12), 2162; https://doi.org/10.3390/sym17122162 - 16 Dec 2025

Viewed by 837

Abstract

Detecting aircraft in Airport Surface Movement Radar (SMR) imagery presents a unique challenge rooted in the conflict between object symmetry and data asymmetry. While aircraft possess strong structural symmetry, their radar signatures are often sparse, incomplete, and highly asymmetric, leading to target loss and position jitter in traditional detection algorithms. To overcome this, we introduce SWCR-YOLO, a keypoint detection framework designed to learn and enforce the target’s implicit structural symmetry from its imperfect radar representation. Our model reconstructs a stable aircraft pose by localizing four keypoints (nose, tail, wingtips) that define its symmetric axes. Based on YOLOv11n, SWCR-YOLO incorporates a MultiScaleStem module and wavelet transforms to effectively extract features from the sparse, asymmetric scatter points, while a Multi-Scale Convolutional Attention (MSCA) module refines salient information. Crucially, training is guided by a Geometric Regularized Keypoint Loss (GRKLoss), which introduces a symmetry-based prior by imposing angular constraints on the keypoints to ensure physically plausible pose estimations. Our symmetry-aware approach, on a real-world SMR dataset, achieves an mAP50 of 88.2% and reduces the trajectory root mean square error by 51.8% compared to MTD-CFAR pipeline methods, from 8.235 m to 3.968 m, demonstrating its effectiveness in handling asymmetric data for robust object tracking. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry in Image Processing and Computer Vision)

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21 pages, 3719 KB

Open AccessArticle

SPIRIT: Symmetry-Prior Informed Diffusion for Thangka Segmentation

by Yukai Xian, Yurui Lee, Liang Yan, Te Shen, Ping Lan, Qijun Zhao and Yi Zhang

Symmetry 2025, 17(10), 1643; https://doi.org/10.3390/sym17101643 - 3 Oct 2025

Cited by 3 | Viewed by 1072

Abstract

Thangka paintings, as intricate forms of Tibetan Buddhist art, present unique challenges for image segmentation due to their densely arranged symbolic elements, complex color patterns, and strong structural symmetry. To address these difficulties, we propose SPIRIT, a structure-aware and prompt-guided diffusion segmentation framework tailored for Thangka images. Our method incorporates a support-query-encoding scheme to exploit limited labeled samples and introduces semantic guided attention fusion to integrate symbolic knowledge into the denoising process. Moreover, we design a symmetry-aware refinement module to explicitly preserve bilateral and radial symmetries, enhancing both accuracy and interpretability. Experimental results on our curated Thangka dataset and the artistic ArtBench benchmark demonstrate that our approach achieves 88.3% mIoU on Thangka and 86.1% mIoU on ArtBench, outperforming the strongest baseline by 6.1% and 5.6% mIoU, respectively. These results confirm that SPIRIT not only captures fine-grained details, but also excels in segmenting structurally complex regions of artistic imagery. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry in Image Processing and Computer Vision)

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22 pages, 3281 KB

Open AccessArticle

A Privacy-Enhancing Image Encryption Algorithm for Securing Medical Images

by Ammar Odeh, Anas Abu Taleb, Tareq Alhajahjeh, Francisco Navarro, Aladdin Ayesh and Miad Faezipour

Symmetry 2025, 17(9), 1470; https://doi.org/10.3390/sym17091470 - 6 Sep 2025

Cited by 5 | Viewed by 2505

Abstract

The growing digitization of healthcare has amplified concerns about the privacy and security of medical images, as conventional encryption methods often fail to provide sufficient protection. To address this gap, we propose a privacy-enhancing image encryption algorithm that integrates SHA-256 hashing, block-wise processing (16 × 16 with zero-padding), DNA encoding with XOR operations, and logistic map-driven key generation into a unified framework. This synergistic design balances efficiency and robustness by embedding data integrity verification, ensuring high sensitivity to initial conditions, and achieving strong diffusion through dynamic DNA rules. Experimental results confirm that the scheme achieves high NPCR (0.997), UACI (0.289), entropy (7.995), and PSNR (27.89 dB), outperforming comparable approaches while maintaining scalability to large image formats and robustness under compression (JPEG quality factors 90 and 70). These findings demonstrate that the proposed method offers an efficient and resilient solution for securing medical images, ensuring confidentiality, integrity, and practical applicability in real-world healthcare environments. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry in Image Processing and Computer Vision)

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26 pages, 2959 KB

Open AccessArticle

A Non-Invasive Gait-Based Screening Approach for Parkinson’s Disease Using Time-Series Analysis

by Hui Chen, Tee Connie, Vincent Wei Sheng Tan, Michael Kah Ong Goh, Nor Izzati Saedon, Ahmad Al-Khatib and Mahmoud Farfoura

Symmetry 2025, 17(9), 1385; https://doi.org/10.3390/sym17091385 - 25 Aug 2025

Cited by 2 | Viewed by 1943

Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that severely impacts motor function, necessitating early detection for effective management. However, current diagnostic methods are expensive and resource-intensive, limiting their accessibility. This study proposes a non-invasive, gait-based screening approach for PD using time-series analysis of video-derived motion data. Gait patterns indicative of PD are analyzed using videos containing walking sequences of PD subjects. The video data are processed via computer vision and human pose estimation techniques to extract key body points. Classification is performed using K-Nearest Neighbors (KNN) and Long Short-Term Memory (LSTM) networks in conjunction with time-series techniques, including Dynamic Time Warping (DTW), Bag of Patterns (BoP), and Symbolic Aggregate Approximation (SAX). KNN classifies based on similarity measures derived from these methods, while LSTM captures complex temporal dependencies. Additionally, Shapelet-based Classification is independently explored for its ability to serve as a self-contained classifier by extracting discriminative motion patterns. On a self-collected dataset (43 instances: 8 PD and 35 healthy), DTW-based classification achieved 88.89% accuracy for both KNN and LSTM. On an external dataset (294 instances: 150 healthy and 144 PD with varying severity), KNN and LSTM achieved 71.19% and 57.63% accuracy, respectively. The proposed approach enhances PD detection through a cost-effective, non-invasive methodology, supporting early diagnosis and disease monitoring. By integrating machine learning with clinical insights, this study demonstrates the potential of AI-driven solutions in advancing PD screening and management. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry in Image Processing and Computer Vision)

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21 pages, 1192 KB

Open AccessArticle

Video Stabilization Algorithm Based on View Boundary Synthesis

by Wenchao Shan, Hejing Zhao, Xin Li, Qian Huang, Chuanxu Jiang, Yiming Wang, Ziqi Chen and Yao Tong

Symmetry 2025, 17(8), 1351; https://doi.org/10.3390/sym17081351 - 19 Aug 2025

Cited by 1 | Viewed by 2484

Abstract

Video stabilization is a critical technology for enhancing visual content quality in dynamic shooting scenarios, especially with the widespread adoption of mobile photography devices and Unmanned Aerial Vehicle (UAV) platforms. While traditional digital stabilization algorithms can improve frame stability by modeling global motion trajectories, they often suffer from excessive cropping or boundary distortion, leading to a significant loss of valid image regions. To address this persistent challenge, we propose the View Out-boundary Synthesis Algorithm (VOSA), a symmetry-aware spatio-temporal consistency framework. By leveraging rotational and translational symmetry principles in motion dynamics, VOSA realizes optical flow field extrapolation through an encoder–decoder architecture and an iterative boundary extension strategy. Experimental results demonstrate that VOSA enhances conventional stabilization by increasing content retention by 6.3% while maintaining a 0.943 distortion score, outperforming mainstream methods in dynamic environments. The symmetry-informed design resolves stability–content conflicts and outperforms mainstream methods in dynamic environments, establishing a new paradigm for full-frame stabilization. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry in Image Processing and Computer Vision)

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25 pages, 9676 KB

Open AccessArticle

You Only Look Once–Aluminum: A Detection Model for Complex Aluminum Surface Defects Based on Improved YOLOv8

by Jiashu Han, Huiye Chen, Yitong Ding, Shudong Zhuang, Chengyu Zhou and Hua Chen

Symmetry 2025, 17(5), 724; https://doi.org/10.3390/sym17050724 - 9 May 2025

Cited by 1 | Viewed by 2483

Abstract

Detecting aluminum defects in industrial environments presents significant challenges related to low-resolution images, subtle damage features, and an imbalance between easy and difficult samples. The You Only Look Once–Aluminum (YOLO-AL) algorithm proposed in this paper addresses these challenges. Firstly, to enhance the model’s performance on low-resolution images and small object detection, as well as to improve its flexibility and adaptability, C2f-US replaces the first two CSP bottleneck with 2 Convolutions (C2f) layers in the original Backbone network. Secondly, to boost multi-scale context capture and strip defect detection, a CPMSCA mechanism with a class-symmetric structure is proposed and integrated at the end of the Backbone network. Thirdly, to efficiently capture both high-level semantics and low-level spatial details, and improve detection of complex aluminum surface defects, ODE-RepGFPN is introduced to replace the entire Neck network. Finally, to address the imbalance between hard and easy samples, Focaler-WIoU is proposed. Extensive experiments conducted on the publicly available AliCloud dataset (APDDD) demonstrate that YOLO-AL achieves 86.5%, 77.8%, and 81.5% for Precision, Recall, and mAP@0.5, respectively, surpassing both the baseline model and other state-of-the-art methods. The model can be integrated with an industrial camera system for the automated inspection of aluminum profiles in a production environment. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry in Image Processing and Computer Vision)

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15 pages, 10789 KB

Open AccessArticle

Deep Double Towers Click Through Rate Prediction Model with Multi-Head Bilinear Fusion

by Yuan Zhang, Xiaobao Cheng, Wei Wei and Yangyang Meng

Symmetry 2025, 17(2), 159; https://doi.org/10.3390/sym17020159 - 22 Jan 2025

Viewed by 3118

Abstract

The click-through rate (CTR) forecast is among the mainstream research directions in the domain of recommender systems, especially in online advertising suggestions. Among them, the multilayer perceptron (MLP) has been extensively utilized as the cornerstone of deep CTR prediction models. However, current neural network-based CTR prediction models commonly employ a single MLP network to capture nonlinear interactions between high-order features, while disregarding the interaction among differentiated features, resulting in poor model performance. Although studies such as DeepFM have proposed dual-branch interaction models to learn complex features, they still fall short of achieving more nuanced feature fusion. To address these challenges, we propose a novel model, the Deep Double Towers model (DDT), which improves the accuracy of CTR prediction through multi-head bilinear fusion while incorporating symmetry in its architecture. Specifically, the DDT model leverages symmetric parallel MLP networks to capture the interactions between differentiated features in a more structured and balanced manner. Furthermore, the multi-head bilinear fusion layer enables refined feature fusion through symmetry-aware operations, ensuring that feature interactions are aligned and symmetrically integrated. Experimental results on publicly available datasets, such as Criteo and Avazu, show that DDT surpasses existing models in improving the accuracy of CTR prediction, with symmetry contributing to more effective and balanced feature fusion. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry in Image Processing and Computer Vision)

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