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: 31 October 2024 | Viewed by 455

Special Issue Editors

School of Astronautics, Beihang University, 102206 Beijing, China
Interests: computer vision; machine learning; medical image processing

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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

Published Papers (1 paper)

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Research

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
Viewed by 343
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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