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Symmetry
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Advances in Image Processing with Symmetry/Asymmetry
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Advances in Image Processing with Symmetry/Asymmetry

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 4339

Special Issue Editors

Dr. Yu Zhang

E-Mail Website
Guest Editor
School of Astronautics, Beihang University, 102206 Beijing, China
Interests: computer vision; machine learning; medical image processing
Dr. Shunli Zhang

E-Mail Website
Guest Editor
School of Software Engineering, Beijing Jiaotong University, 100044 Beijing, China
Interests: machine learning and deep learning; image and video processing and analysis; intelligent cognition and decision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We warmly invite researchers and practitioners to contribute their expertise to a Special Issue dedicated to the latest breakthroughs in image processing, with a specific focus on symmetry and asymmetry. This Special Issue aims to explore innovative methodologies, algorithms, and applications that harness symmetry and asymmetry properties for image processing, analysis, and interpretation.

Topics of interest span a broad spectrum and include, but are not limited to, the following:

  1. Symmetry- and asymmetry-driven methodologies for image enhancement, restoration, or fusion.
  2. Feature extraction or pattern recognition guided by symmetry and asymmetry principles.
  3. Leveraging symmetry and asymmetry in visual object detection and tracking algorithms.
  4. Innovative deep learning architectures inspired by symmetry and asymmetry principles for various image processing tasks.
  5. Investigation into the role of symmetry and asymmetry in remote sensing image analysis.
  6. Application of symmetry and asymmetry principles in the realm of medical image analysis.

We eagerly welcome original research articles that significantly advance the understanding and application of symmetry and asymmetry principles in the field of image processing. Submissions should demonstrate substantial contributions, supported by rigorous experimental validation and insights into potential real-world applications. Join us in pushing the boundaries of image processing through the exploration of symmetry and asymmetry paradigms.

Dr. Yu Zhang
Dr. Shunli Zhang
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

  • Image processing
  • symmetry
  • asymmetry
  • computer vision
  • feature extraction
  • pattern recognition
  • deep learning
  • medical imaging
  • remote sensing

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

Published Papers (4 papers)

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Research

23 pages, 13904 KiB  
Article
Symmetric Model for Predicting Homography Matrix Between Courts in Co-Directional Multi-Frame Sequence
by Pan Zhang, Jiangtao Luo and Xupeng Liang
Symmetry 2025, 17(6), 832; https://doi.org/10.3390/sym17060832 - 27 May 2025
Viewed by 333
Abstract
The homography matrix is essential for perspective transformation across consecutive video frames. While existing methods are effective when the visual content between paired images remains largely unchanged, they rely on substantial, high-quality annotated data for a multi-frame court sequence with content variation. To [...] Read more.
The homography matrix is essential for perspective transformation across consecutive video frames. While existing methods are effective when the visual content between paired images remains largely unchanged, they rely on substantial, high-quality annotated data for a multi-frame court sequence with content variation. To address this limitation and enhance homography matrix predictions in competitive sports images, a new symmetric stacked neural network model is proposed. The model first leverages the mutual invertibility of bidirectional homography matrices to improve prediction accuracy between paired images. Secondly, by theoretically validating and leveraging the decomposability of the homography matrix, the model significantly reduces the amount of data annotation required for continuous frames within the same shooting direction. Experimental evaluations on datasets for court homography transformations in sports, such as ice hockey, basketball, and handball, show that the proposed symmetric model achieves superior accuracy in predicting homography matrices, even when only one-third of the frames are annotated. Comparisons with seven related methods further highlight the exceptional performance of the proposed model. Full article
(This article belongs to the Special Issue Advances in Image Processing with Symmetry/Asymmetry)
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17 pages, 9440 KiB  
Article
RACFME: Object Tracking in Satellite Videos by Rotation Adaptive Correlation Filters with Motion Estimations
by Xiongzhi Wu, Haifeng Zhang, Chao Mei, Jiaxin Wu and Han Ai
Symmetry 2025, 17(4), 608; https://doi.org/10.3390/sym17040608 - 16 Apr 2025
Viewed by 303
Abstract
Video satellites provide high-temporal-resolution remote sensing images that enable continuous monitoring of the ground for applications such as target tracking and airport traffic detection. In this paper, we address the problems of object occlusion and the tracking of rotating objects in satellite videos [...] Read more.
Video satellites provide high-temporal-resolution remote sensing images that enable continuous monitoring of the ground for applications such as target tracking and airport traffic detection. In this paper, we address the problems of object occlusion and the tracking of rotating objects in satellite videos by introducing a rotation-adaptive tracking algorithm for correlation filters with motion estimation (RACFME). Our algorithm proposes the following improvements over the KCF method: (a) A rotation-adaptive feature enhancement module (RA) is proposed to obtain the rotated image block by affine transformation combined with the target rotation direction prior, which overcomes the disadvantage of HOG features lacking rotation adaptability, improves tracking accuracy while ensuring real-time performance, and solves the problem of tracking failure due to insufficient valid positive samples when tracking rotating targets. (b) Based on the correlation between peak response and occlusion, an occlusion detection method for vehicles and ships in satellite video is proposed. (c) Motion estimations are achieved by combining Kalman filtering with motion trajectory averaging, which solves the problem of tracking failure in the case of object occlusion. The experimental results show that the proposed RACFME algorithm can track a moving target with a 95% success score, and the RA module and ME both play an effective role. Full article
(This article belongs to the Special Issue Advances in Image Processing with Symmetry/Asymmetry)
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18 pages, 41805 KiB  
Article
Research on Unsupervised Feature Point Prediction Algorithm for Multigrid Image Stitching
by Jun Li, Yufeng Chen and Aiming Mu
Symmetry 2024, 16(8), 1064; https://doi.org/10.3390/sym16081064 - 18 Aug 2024
Viewed by 1397
Abstract
The conventional feature point-based image stitching algorithm exhibits inconsistencies in the quality of feature points across diverse scenes. This may result in the deterioration of the alignment effect or even the inability to align two images. To address this issue, this paper presents [...] Read more.
The conventional feature point-based image stitching algorithm exhibits inconsistencies in the quality of feature points across diverse scenes. This may result in the deterioration of the alignment effect or even the inability to align two images. To address this issue, this paper presents an unsupervised multigrid image alignment method that integrates the conventional feature point-based image alignment algorithm with deep learning techniques. The method postulates that the feature points are uniformly distributed in the image and employs a deep learning network to predict their displacements, thereby enhancing the robustness of the feature points. Furthermore, the precision of image alignment is enhanced through the parameterization of APAP (As-projective-as-possible image stitching with moving DLT) multigrid deformation. Ultimately, based on the symmetry exhibited by the homography matrix and its inverse matrix throughout the projection process, image chunking inverse warping is introduced to obtain the stitched images for the multigrid deep learning network. Additionally, the mesh shape-preserving loss is introduced to constrain the shape of the multigrid. The experimental results demonstrate that in the real-world UDIS-D dataset, the method achieves notable improvements in feature point matching and homography estimation tasks, and exhibits superior alignment performance on the traditional image stitching dataset. Full article
(This article belongs to the Special Issue Advances in Image Processing with Symmetry/Asymmetry)
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19 pages, 15303 KiB  
Article
A Novel Self-Adaptive Deformable Convolution-Based U-Net for Low-Light Image Denoising
by Hua Wang, Jianzhong Cao, Huinan Guo and Cheng Li
Symmetry 2024, 16(6), 646; https://doi.org/10.3390/sym16060646 - 23 May 2024
Cited by 1 | Viewed by 1548
Abstract
Capturing images under extremely low-light conditions usually suffers from various types of noise due to the limited photon and low signal-to-noise ratio (SNR), which makes low-light denoising a challenging task in the field of imaging technology. Nevertheless, existing methods primarily focus on investigating [...] Read more.
Capturing images under extremely low-light conditions usually suffers from various types of noise due to the limited photon and low signal-to-noise ratio (SNR), which makes low-light denoising a challenging task in the field of imaging technology. Nevertheless, existing methods primarily focus on investigating the precise modeling of real noise distributions while neglecting improvements in the noise modeling capabilities of learning models. To address this situation, a novel self-adaptive deformable-convolution-based U-Net (SD-UNet) model is proposed in this paper. Firstly, deformable convolution is employed to tackle noise patterns with different geometries, thus extracting more reliable noise representations. After that, a self-adaptive learning block is proposed to enable the network to automatically select appropriate learning branches for noise with different scales. Finally, a novel structural loss function is leveraged to evaluate the difference between denoised and clean images. The experimental results on multiple public datasets validate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Advances in Image Processing with Symmetry/Asymmetry)
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