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Symmetry
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Symmetry and Its Applications in Image Processing
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Symmetry and Its Applications in Image Processing

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 5119

Special Issue Editors

Dr. José Luis Vázquez-Noguera

E-Mail Website
Guest Editor
Facultad Politécnica, Universidad Nacional de Asunción, San Lorenzo, Paraguay
Interests: image processing; mathematical morphology; computer vision
Dr. Horacio Legal-Ayala

E-Mail Website
Guest Editor
Facultad Politécnica, Universidad Nacional de Asunción, San Lorenzo 2111, Paraguay
Interests: image processing; mathematical morphology; computer vision
Dr. Julio César Mello-Román

E-Mail Website
Guest Editor
Facultad Politécnica, Universidad Nacional de Asunción, San Lorenzo 111421, Paraguay
Interests: image enhancement; mathematical morphology; image processing and analysis; artificial intelligence

Special Issue Information

Dear Colleagues,

The Special Issue, entitled “Symmetry and Its Applications in Image Processing”, delves into the fascinating relationship between symmetry phenomena and image processing, exploring how symmetry concepts enrich and enhance image processing techniques. Symmetry, as a fundamental principle in various disciplines, plays a pivotal role in the understanding and manipulation of visual data. This Special Issue aims to provide a comprehensive exploration of the multifaceted ways in which symmetry influences image processing methodologies, from fundamental principles to innovative applications.

The articles in this Special Issue will cover a wide range of topics, including symmetry-based image enhancement techniques, symmetrical algorithms for pattern recognition, computational methods for symmetry detection, and the utilization of symmetric patterns in feature extraction. By shedding light on these diverse aspects of symmetry in image processing, this Special Issue seeks to contribute to the advancement of research in this dynamic field and inspire new avenues of inquiry and innovation.

Dr. José Luis Vázquez-Noguera
Dr. Horacio Legal-Ayala
Dr. Julio César Mello-Román
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

  • symmetry
  • image processing
  • visual data analysis
  • symmetric algorithms
  • pattern recognition
  • computational imaging
  • symmetry detection
  • feature extraction
  • symmetric patterns
  • symmetry-based transformations

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Published Papers (5 papers)

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Research

22 pages, 4611 KiB  
Article
MMC-YOLO: A Lightweight Model for Real-Time Detection of Geometric Symmetry-Breaking Defects in Wind Turbine Blades
by Caiye Liu, Chao Zhang, Xinyu Ge, Xunmeng An and Nan Xue
Symmetry 2025, 17(8), 1183; https://doi.org/10.3390/sym17081183 - 24 Jul 2025
Abstract
Performance degradation of wind turbine blades often stems from geometric asymmetry induced by damage. Existing methods for assessing damage face challenges in balancing accuracy and efficiency due to their limited ability to capture fine-grained geometric asymmetries associated with multi-scale damage under complex background [...] Read more.
Performance degradation of wind turbine blades often stems from geometric asymmetry induced by damage. Existing methods for assessing damage face challenges in balancing accuracy and efficiency due to their limited ability to capture fine-grained geometric asymmetries associated with multi-scale damage under complex background interference. To address this, based on the high-speed detection model YOLOv10-N, this paper proposes a novel detection model named MMC-YOLO. First, the Multi-Scale Perception Gated Convolution (MSGConv) Module was designed, which constructs a full-scale receptive field through multi-branch fusion and channel rearrangement to enhance the extraction of geometric asymmetry features. Second, the Multi-Scale Enhanced Feature Pyramid Network (MSEFPN) was developed, integrating dynamic path aggregation and an SENetv2 attention mechanism to suppress background interference and amplify damage response. Finally, the Channel-Compensated Filtering (CCF) module was constructed to preserve critical channel information using a dynamic buffering mechanism. Evaluated on a dataset of 4818 wind turbine blade damage images, MMC-YOLO achieves an 82.4% mAP [0.5:0.95], representing a 4.4% improvement over the baseline YOLOv10-N model, and a 91.1% recall rate, an 8.7% increase, while maintaining a lightweight parameter count of 4.2 million. This framework significantly enhances geometric asymmetry defect detection accuracy while ensuring real-time performance, meeting engineering requirements for high efficiency and precision. Full article
(This article belongs to the Special Issue Symmetry and Its Applications in Image Processing)
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24 pages, 824 KiB  
Article
MMF-Gait: A Multi-Model Fusion-Enhanced Gait Recognition Framework Integrating Convolutional and Attention Networks
by Kamrul Hasan, Khandokar Alisha Tuhin, Md Rasul Islam Bapary, Md Shafi Ud Doula, Md Ashraful Alam, Md Atiqur Rahman Ahad and Md. Zasim Uddin
Symmetry 2025, 17(7), 1155; https://doi.org/10.3390/sym17071155 - 19 Jul 2025
Viewed by 255
Abstract
Gait recognition is a reliable biometric approach that uniquely identifies individuals based on their natural walking patterns. It is widely used to recognize individuals who are challenging to camouflage and do not require a person’s cooperation. The general face-based person recognition system often [...] Read more.
Gait recognition is a reliable biometric approach that uniquely identifies individuals based on their natural walking patterns. It is widely used to recognize individuals who are challenging to camouflage and do not require a person’s cooperation. The general face-based person recognition system often fails to determine the offender’s identity when they conceal their face by wearing helmets and masks to evade identification. In such cases, gait-based recognition is ideal for identifying offenders, and most existing work leverages a deep learning (DL) model. However, a single model often fails to capture a comprehensive selection of refined patterns in input data when external factors are present, such as variation in viewing angle, clothing, and carrying conditions. In response to this, this paper introduces a fusion-based multi-model gait recognition framework that leverages the potential of convolutional neural networks (CNNs) and a vision transformer (ViT) in an ensemble manner to enhance gait recognition performance. Here, CNNs capture spatiotemporal features, and ViT features multiple attention layers that focus on a particular region of the gait image. The first step in this framework is to obtain the Gait Energy Image (GEI) by averaging a height-normalized gait silhouette sequence over a gait cycle, which can handle the left–right gait symmetry of the gait. After that, the GEI image is fed through multiple pre-trained models and fine-tuned precisely to extract the depth spatiotemporal feature. Later, three separate fusion strategies are conducted, and the first one is decision-level fusion (DLF), which takes each model’s decision and employs majority voting for the final decision. The second is feature-level fusion (FLF), which combines the features from individual models through pointwise addition before performing gait recognition. Finally, a hybrid fusion combines DLF and FLF for gait recognition. The performance of the multi-model fusion-based framework was evaluated on three publicly available gait databases: CASIA-B, OU-ISIR D, and the OU-ISIR Large Population dataset. The experimental results demonstrate that the fusion-enhanced framework achieves superior performance. Full article
(This article belongs to the Special Issue Symmetry and Its Applications in Image Processing)
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27 pages, 13146 KiB  
Article
Underwater-Image Enhancement Based on Maximum Information-Channel Correction and Edge-Preserving Filtering
by Wei Liu, Jingxuan Xu, Siying He, Yongzhen Chen, Xinyi Zhang, Hong Shu and Ping Qi
Symmetry 2025, 17(5), 725; https://doi.org/10.3390/sym17050725 - 9 May 2025
Viewed by 741
Abstract
The properties of light propagation underwater typically cause color distortion and reduced contrast in underwater images. In addition, complex underwater lighting conditions can result in issues such as non-uniform illumination, spotting, and noise. To address these challenges, we propose an innovative underwater-image enhancement [...] Read more.
The properties of light propagation underwater typically cause color distortion and reduced contrast in underwater images. In addition, complex underwater lighting conditions can result in issues such as non-uniform illumination, spotting, and noise. To address these challenges, we propose an innovative underwater-image enhancement (UIE) approach based on maximum information-channel compensation and edge-preserving filtering techniques. Specifically, we first develop a channel information transmission strategy grounded in maximum information preservation principles, utilizing the maximum information channel to improve the color fidelity of the input image. Next, we locally enhance the color-corrected image using guided filtering and generate a series of globally contrast-enhanced images by applying gamma transformations with varying parameter values. In the final stage, the enhanced image sequence is decomposed into low-frequency (LF) and high-frequency (HF) components via side-window filtering. For the HF component, a weight map is constructed by calculating the difference between the current exposedness and the optimum exposure. For the LF component, we derive a comprehensive feature map by integrating the brightness map, saturation map, and saliency map, thereby accurately assessing the quality of degraded regions in a manner that aligns with the symmetry principle inherent in human vision. Ultimately, we combine the LF and HF components through a weighted summation process, resulting in a high-quality underwater image. Experimental results demonstrate that our method effectively achieves both color restoration and contrast enhancement, outperforming several State-of-the-Art UIE techniques across multiple datasets. Full article
(This article belongs to the Special Issue Symmetry and Its Applications in Image Processing)
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21 pages, 6744 KiB  
Article
MADC-Net: Densely Connected Network with Multi-Attention for Metal Surface Defect Segmentation
by Xiaokang Ding, Xiaoliang Jiang and Sheng Wang
Symmetry 2025, 17(4), 518; https://doi.org/10.3390/sym17040518 - 29 Mar 2025
Viewed by 351
Abstract
The quality of metal products plays a crucial role in determining their overall performance, reliability and safety. Therefore, timely and effective detection of metal surface defects is of great significance. For this purpose, we present a densely connected network with multi-attention for metal [...] Read more.
The quality of metal products plays a crucial role in determining their overall performance, reliability and safety. Therefore, timely and effective detection of metal surface defects is of great significance. For this purpose, we present a densely connected network with multi-attention for metal surface defect segmentation, called MADC-Net. Firstly, we selected ResNet50 as the encoder due to its robust performance. To capture richer contextual information from the defect feature map, we designed a densely connected network and incorporated the multi-attention of a CESConv module, an efficient channel attention module (ECAM), and a simple attention module (SimAM) into the decoder. In addition, in the final stage of the decoder, we introduced a reconfigurable efficient attention module (REAM) to reduce redundant calculations and enhance the detection of complex defect structures. Finally, a series of comprehensive comparative and ablation experiments were conducted on the publicly available SD-saliency-900 dataset and our self-constructed Bearing dataset, all of which validated that our proposed method was effective and reliable in defect segmentation. Specifically, the Dice and Jaccard scores for the SD-saliency-900 dataset were 88.82% and 79.96%. In comparison, for the Bearing dataset, the Dice score was 78.24% and the Jaccard score was 64.74%. Full article
(This article belongs to the Special Issue Symmetry and Its Applications in Image Processing)
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19 pages, 4486 KiB  
Article
Pear Object Detection in Complex Orchard Environment Based on Improved YOLO11
by Mingming Zhang, Shutong Ye, Shengyu Zhao, Wei Wang and Chao Xie
Symmetry 2025, 17(2), 255; https://doi.org/10.3390/sym17020255 - 8 Feb 2025
Cited by 7 | Viewed by 3015
Abstract
To address the issues of low detection accuracy and poor adaptability in complex orchard environments (such as varying lighting conditions, branch and leaf occlusion, fruit overlap, and small targets), this paper proposes an improved pear detection model based on YOLO11, called YOLO11-Pear. First, [...] Read more.
To address the issues of low detection accuracy and poor adaptability in complex orchard environments (such as varying lighting conditions, branch and leaf occlusion, fruit overlap, and small targets), this paper proposes an improved pear detection model based on YOLO11, called YOLO11-Pear. First, to improve the model’s capability in detecting occluded pears, the C2PSS module is introduced to replace the original C2PSA module. Second, a small target detection layer is added to improve the model’s ability to detect small pears. Finally, the upsampling process is replaced with DySample, which not only maintains a high efficiency but also improves the processing speed and expands the model’s application range. To validate the effectiveness of the model, a dataset of images of Qiu Yue pears and Cui Guan pears was constructed. The experimental results showed that the improved YOLO11-Pear model achieved precision, recall, mAP50, and mAP50–95 values of 96.3%, 84.2%, 92.1%, and 80.2%, respectively, outperforming YOLO11n by 3.6%, 1%, 2.1%, and 3.2%. With only a 2.4% increase in the number of parameters compared to the original model, YOLO11-Pear enables fast and accurate pear detection in complex orchard environments. Full article
(This article belongs to the Special Issue Symmetry and Its Applications in Image Processing)
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