Symmetry in Image Processing: Current Advances and Applications

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 854

Special Issue Editors


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Guest Editor
Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Culhuacan, Instituto Politécnico Nacional, Av. Santa Ana No. 1000, Col. San Francisco Culhucan, Mexico City 04430, Mexico
Interests: image and signal processing; invisible-visible watermarking; steganography; steganalysis; artificial neural networks; deep learning; ownership authentication; security information; data protection; cybersecurity in power electronics; artificial neural network controller

E-Mail Website
Guest Editor
Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Culhuacan, Instituto Politécnico Nacional, Av. Santa Ana No. 1000, Col. San Francisco Culhucan, Mexico City 04430, Mexico
Interests: image processing; signal processing; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are delighted to announce the launch of a Special Issue titled ‘Symmetry in Image Processing: Current Advances and Applications.’

The field of image processing has become one of the most significant areas of research in recent years, driven by the growing demand for intelligent visual analysis and automation. Through advanced techniques such as filtering, segmentation, enhancement, and restoration, image processing improves image quality, extracts meaningful information, detects complex patterns, and supports critical decision-making processes. In healthcare, it assists in disease diagnosis, medical imaging analysis, and surgical planning; in security, it plays a key role in facial recognition, surveillance, and threat detection systems. As the volume of digital images continues to increase exponentially, the need for efficient, accurate, and real-time image processing becomes even more essential for innovation and technological advancement worldwide.

Current developments in this field are closely linked to image protection, computer vision, medical image management, and spatial and frequency domain analysis. Industrial applications of image processing cover a wide range of areas, including robotics, manufacturing, automotive systems, and quality control, among others. Furthermore, recent breakthroughs in deep learning and machine learning have revolutionized the field, enhancing capabilities in pattern recognition, object detection, symmetry estimation, and facial recognition. These innovations have expanded the potential of image processing across numerous scientific and industrial domains.

This Special Issue aims to highlight cutting-edge research in symmetry and image processing, fostering the exchange of ideas and methodologies. Research areas may include (but are not limited to) the following:

  • image processing
  • image segmentation analysis
  • image protection
  • image segmentation, enhancement and restoration
  • symmetry filtering and multiresolution on image processing
  • symmetry in image transformation
  • computing vision
  • medical image
  • symmetry on structural analysis
  • feature extraction
  • object detection and recognition
  • machine learning
  • deep learning

Dr. Oswaldo Ulises Juarez-Sandoval
Prof. Dr. Mariko Nakano-Miyatake
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

  • image processing
  • image segmentation analysis
  • image protection
  • image segmentation, enhancement and restoration
  • symmetry filtering and multiresolution on image processing
  • symmetry in image transformation
  • computing vision
  • medical image
  • symmetry on structural analysis
  • feature extraction
  • object detection and recognition
  • machine learning
  • deep learning

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Published Papers (1 paper)

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Research

24 pages, 3324 KB  
Communication
An Edge-Preserving Hybrid Filter Based on UFIR Filters for Reducing Gaussian Noise in Digital Images
by Erika Mendoza-Salvador, Luis J. Morales-Mendoza, Mario Gonzalez-Lee, Eli G. Pale-Ramon, Hector Vazquez-Leal, Hector Perez-Meana and Rene F. Vazquez-Bautista
Symmetry 2026, 18(5), 871; https://doi.org/10.3390/sym18050871 - 21 May 2026
Viewed by 158
Abstract
In this paper, we propose a new digital filtering approach based on the FIR-Median Hybrid (FMH) structure, which incorporates an Unbiased Finite Impulse Response (UFIR) filter as its core component. The proposed filter employs spatially symmetric window configurations to reduce Gaussian noise while [...] Read more.
In this paper, we propose a new digital filtering approach based on the FIR-Median Hybrid (FMH) structure, which incorporates an Unbiased Finite Impulse Response (UFIR) filter as its core component. The proposed filter employs spatially symmetric window configurations to reduce Gaussian noise while preserving edges in images. Although the scientific community is rapidly adopting machine-learning- and deep-learning-based filters, there are several reasons to continue developing filters based on traditional methods. For example, these methods are well understood and rely on a strong mathematical foundation. Moreover, the structure of the proposed filter is simple; thus, this type of filter may be appealing to engineers unfamiliar with the machine-learning field. The performance of the proposed filter was assessed using two datasets: the first consisted of a set of artificial binary images, and the second comprised a subset of the BOWS image dataset. We conducted three main experiments. In the first experiment, we fine-tuned the filter considering three window-shape configurations. In the second experiment, Gaussian noise was added to the images, and the proposed filter was compared against other filters using edge-preservation-oriented metrics such as the Structural Similarity Index Measure (SSIM), the Normalized Step Edge Response (NSER), and the Gradient Conduction Mean Square Error (GcMSE), among others. The third experiment evaluated the performance of the best-performing window-shape configurations. This final test was assessed quantitatively using the Friedman test to identify the best-performing structure, whereas qualitative assessment was conducted using a Mean Opinion Score (MOS) test. The results show that the proposed filter achieved improved performance according to the PSNR, SNR, RMSE, and GcMSE metrics. These findings suggest that the proposed filter can be used in practical applications such as image enhancement, computer vision, and edge-detection-based preprocessing. Full article
(This article belongs to the Special Issue Symmetry in Image Processing: Current Advances and Applications)
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