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Symmetry
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Symmetry in Image Processing: Novel Topics and Advancements
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Symmetry in Image Processing: Novel Topics and Advancements

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

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 2864

Special Issue Editor

Prof. Dr. Jie Yang

E-Mail Website
Guest Editor
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200400, China
Interests: artificial intelligence; machine learning; computer vision; medical image analysis and 3D construction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Symmetry is a fundamental concept in image processing that has been widely studied in recent years due to its potential applications in various fields. It has been used for image recognition, detection, and analysis, as well as for disease diagnosis and treatment planning in medical image processing. Thus, the study of symmetry in image processing is an active research area of crucial importance that requires further attention and investigations.  

This Special Issue, ‘Symmetry in Image Processing: Novel Topics and Advancements’ aims to bring together researchers and practitioners from various disciplines to share their latest findings and developments in the field of symmetry as well as it applications in image analysis. We are particularly interested in submissions that explore novel applications of machine and deep learning approaches; however, we are open to papers investigating other image processing techniques as well. Some potential areas of interest include methods for dealing with a low number (lack) of annotations; optimal/efficient approaches to procure annotations; scalable methods for multi-organ, multi-tissue analysis applications; approaches to deal with non-normalized sequences/imaging data; and techniques to gather population information.

We welcome submissions on topics including, but not limited to, the following:

  • Symmetric image filtering, enhancement, and smoothing;
  • Symmetric image analysis, segmentation, recognition, detection and understanding;
  • Image representation, compression, and coding;
  • Applications of symmetry in medical imaging, acquisition, reconstruction, denoising, super-resolution, segmentation, registration, tracking, and others;
  • Applications of symmetry in different medical image modalities, including MR, X-ray, PET, and US imaging (but excluding biological or microscopy imaging).

All submissions will be subjected to a rigorous peer-review process to ensure their quality and originality. Accepted papers will be published online and will be accessible to all readers.

Prof. Dr. Jie Yang
Guest Editor

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.

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

Published Papers (2 papers)

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14 pages, 10252 KiB  
Article
A New Log-Transform Histogram Equalization Technique for Deep Learning-Based Document Forgery Detection
by Yong-Yeol Bae, Dae-Jea Cho and Ki-Hyun Jung
Symmetry 2025, 17(3), 395; https://doi.org/10.3390/sym17030395 - 5 Mar 2025
Viewed by 535
Abstract
Recent advancements in image processing technology have positively impacted some fields, such as image, document, and video production. However, the negative implications of these advancements have also increased, with document image manipulation being a prominent issue. Document image manipulation involves the forgery or [...] Read more.
Recent advancements in image processing technology have positively impacted some fields, such as image, document, and video production. However, the negative implications of these advancements have also increased, with document image manipulation being a prominent issue. Document image manipulation involves the forgery or alteration of documents like receipts, invoices, various certificates, and confirmations. The use of such manipulated documents can cause significant economic and social disruption. To prevent these issues, various methods for the detection of forged document images are being researched, with recent proposals focused on deep learning techniques. An essential aspect of using deep learning to detect manipulated documents is to enhance or augment the characteristics of document images before inputting them into a model. Enhancing the distinctive features of manipulated documents before inputting them into a deep learning model is crucial to achieve high accuracy. One crucial characteristic of document images is their inherent symmetrical patterns, such as consistent text alignment, structural balance, and uniform pixel distribution. This study investigates document forgery detection through a symmetry-aware approach. By focusing on the symmetric structures found in document layouts and pixel distribution, the proposed LTHE technique enhances feature extraction in deep learning-based models. Therefore, this study proposes a new image enhancement technique based on the results of three general-purpose CNN models to enhance the characteristics of document images and achieve high accuracy in deep learning-based forgery detection. The proposed LTHE (Log-Transform Histogram Equalization) technique increases low pixel values through log transformation and increases image contrast by performing histogram equalization to make the features of the image more prominent. Experimental results show that the proposed LTHE technique achieves higher accuracy when compared to other enhancement methods, indicating its potential to aid the development of deep learning-based forgery detection algorithms in the future. Full article
(This article belongs to the Special Issue Symmetry in Image Processing: Novel Topics and Advancements)
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21 pages, 1666 KiB  
Article
Critical Information Mining Network: Identifying Crop Diseases in Noisy Environments
by Yi Shao, Wenzhong Yang, Zhifeng Lu, Haokun Geng and Danny Chen
Symmetry 2024, 16(6), 652; https://doi.org/10.3390/sym16060652 - 24 May 2024
Viewed by 1340
Abstract
When agricultural experts explore the use of artificial intelligence technology to identify and detect crop diseases, they mainly focus on the research of a stable environment, but ignore the problem of noise in the process of image acquisition in real situations. To solve [...] Read more.
When agricultural experts explore the use of artificial intelligence technology to identify and detect crop diseases, they mainly focus on the research of a stable environment, but ignore the problem of noise in the process of image acquisition in real situations. To solve this problem, we propose an innovative solution called the Critical Information Mining Network (CIMNet). Compared with traditional models, CIMNet has higher recognition accuracy and wider application scenarios. The network has a good effect on crop disease recognition under noisy environments, and can effectively deal with the interference of noise to the recognition effect in actual farmland scenes. Consider that the shape of the leaves can be symmetrical or asymmetrical.First, we introduce the Non-Local Attention Module (Non-Local), which uses a unique self-attention mechanism to fully capture the context information of the image. The module overcomes the limitation of traditional convolutional neural networks that only rely on local features and ignore global features. Global features are particularly important when the image is disturbed by noise. Non-Local improves a more comprehensive visual understanding of crop disease recognition. Secondly, we have innovatively designed a Multi-scale Critical Information Fusion Module (MSCM). The module uses the Key Information Extraction Module (KIB) to dig into the shallow key features in the network deeply. The shallow key features strengthen the feature perception of the model to the noise image through texture and contour information, and then the shallow key features and deep features are fused to enrich the original deep feature information of the network. Finally, we conducted experiments on two public datasets, and the results showed that the accuracy of our model in crop disease identification under a noisy environment was significantly improved. At the same time, our model also showed excellent performance under stable conditions. The results of this study provide favorable support for the improvement of crop production efficiency. Full article
(This article belongs to the Special Issue Symmetry in Image Processing: Novel Topics and Advancements)
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