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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 4395

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

Published Papers (3 papers)

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Research

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 619
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 318
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 2775
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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