Image Processing and Symmetry: Topics and Applications

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 12503

Special Issue Editor


E-Mail Website
Guest Editor
School of Statistics and Data Science, Nankai University, Tianjin 300071, China
Interests: computer vision; image restoration and enhancement

Special Issue Information

Dear Colleagues,

Image processing has witnessed great development in the past few years. The progress of image formation and the advancement of mobile intelligence devices have led to the wide application of research findings in computer vision to industrial production and daily life, simultaneously promoting breakthrough in symmetry image processing techniques. Artificial intelligence (AI) technologies such as deep symmetric neural networks endow learnable strength to the symmetry image processing field. With the great power of learning ability of neural networks, many computer vision applications can be more focused and achieve higher accuracy by processing information from a huge flow of data. However, the training process and usage of symmetric deep neural networks require enormous computation resources, restricting its speed performance and its application on clients. Lightweight networks, which mainly focus on controlling the scale of the network and on speeding up network processing, are therefore proposed and have seen fast growth in recent years. This Special Issue introduces research on symmetric and lightweight neural networks for fast image processing. Some of these topics are the following:

  • Symmetric and lightweight deep neural networks for image processing;
  • Fast image denoising, super resolution, deblurring, etc.;
  • Fast image object detection/segmentation;
  • Fast and symmetric image style transfer.

Dr. Jun Xu
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.

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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
  • deep learning
  • lightweight network
  • super resolution
  • image denoising
  • image deblurring

Published Papers (9 papers)

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Research

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24 pages, 69336 KiB  
Article
A Non-Convex Fractional-Order Differential Equation for Medical Image Restoration
by Chenwei Li and Donghong Zhao
Symmetry 2024, 16(3), 258; https://doi.org/10.3390/sym16030258 - 20 Feb 2024
Viewed by 581
Abstract
We propose a new non-convex fractional-order Weber multiplicative denoising variational generalized function, which leads to a new fractional-order differential equation, and prove the existence of a unique solution to this equation. Furthermore, the model is solved using the partial differential equation (PDE) method [...] Read more.
We propose a new non-convex fractional-order Weber multiplicative denoising variational generalized function, which leads to a new fractional-order differential equation, and prove the existence of a unique solution to this equation. Furthermore, the model is solved using the partial differential equation (PDE) method and the alternating direction multiplier method (ADMM) to verify the theoretical results. The proposed model is tested on some symmetric and asymmetric medical computerized tomography (CT) images, and the experimental results show that the combination of the fractional-order differential equation and the Weber function has better performance in medical image restoration than the traditional model. Full article
(This article belongs to the Special Issue Image Processing and Symmetry: Topics and Applications)
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27 pages, 14350 KiB  
Article
Innovative Dual-Stage Blind Noise Reduction in Real-World Images Using Multi-Scale Convolutions and Dual Attention Mechanisms
by Ziaur Rahman, Muhammad Aamir, Jameel Ahmed Bhutto, Zhihua Hu and Yurong Guan
Symmetry 2023, 15(11), 2073; https://doi.org/10.3390/sym15112073 - 15 Nov 2023
Viewed by 919
Abstract
The distribution of real noise in images can disrupt the inherent symmetry present in many natural visuals, thus making its effective removal a paramount challenge. However, traditional denoising methods often require tedious manual parameter tuning, and a significant portion of deep learning-driven techniques [...] Read more.
The distribution of real noise in images can disrupt the inherent symmetry present in many natural visuals, thus making its effective removal a paramount challenge. However, traditional denoising methods often require tedious manual parameter tuning, and a significant portion of deep learning-driven techniques have proven inadequate for real noise. Moreover, the efficiency of end-to-end algorithms in restoring symmetrical patterns in noisy images remains questionable. To harness the principles of symmetry for improved denoising, we introduce a dual deep learning model with a focus on preserving and leveraging symmetrical patterns in real images. Our methodology operates in two stages. In the first, we estimate the noise level using a four-layer neural network, thereby aiming to capture the underlying symmetrical structures of the original image. To enhance the extraction of symmetrical features and overall network performance, a dual attention mechanism is employed before the final convolutional layer. This innovative module adaptively assigns weights to features across different channels, thus emphasizing symmetry-preserving elements. The subsequent phase is devoted to non-blind denoising. It integrates the estimated noise level and the original image, thus targeting the challenge of denoising while preserving symmetrical patterns. Here, a multi-scale architecture is used, thereby amalgamating image features into two branches. The first branch taps into dilation convolution, thus amplifying the receptive field without introducing new parameters and making it particularly adept at capturing broad symmetrical structures. In contrast, the second branch employs a standard convolutional layer to focus on finer symmetrical details. By harnessing varied receptive fields, our method can recognize and restore image symmetries across different scales. Crucial skip connections are embedded within this multi-scale setup, thus ensuring that symmetrical image data is retained as the network deepens. Experimental evaluations, conducted on four benchmark training sets and 12 test datasets, juxtaposed with over 20 contemporary models based on the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics, underscore our model’s prowess in not only denoising but also in preserving and accentuating symmetrical elements, thereby setting a new gold standard in the field. Full article
(This article belongs to the Special Issue Image Processing and Symmetry: Topics and Applications)
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23 pages, 7015 KiB  
Article
A Symmetrical Approach to Brain Tumor Segmentation in MRI Using Deep Learning and Threefold Attention Mechanism
by Ziaur Rahman, Ruihong Zhang and Jameel Ahmed Bhutto
Symmetry 2023, 15(10), 1912; https://doi.org/10.3390/sym15101912 - 12 Oct 2023
Cited by 2 | Viewed by 1157
Abstract
The symmetrical segmentation of brain tumor images is crucial for both clinical diagnosis and computer-aided prognosis. Traditional manual methods are not only asymmetrical in terms of efficiency but also prone to errors and lengthy processing. A significant barrier to the process is the [...] Read more.
The symmetrical segmentation of brain tumor images is crucial for both clinical diagnosis and computer-aided prognosis. Traditional manual methods are not only asymmetrical in terms of efficiency but also prone to errors and lengthy processing. A significant barrier to the process is the complex interplay between the deep learning network for MRI brain tumor imaging and the harmonious compound of both local and global feature information, which can throw off the balance in segmentation accuracy. Addressing this asymmetry becomes essential for precise diagnosis. In answer to this challenge, we introduce a balanced, end-to-end solution for brain tumor segmentation, incorporating modifications that mirror the U-Net architecture, ensuring a harmonious flow of information. Beginning with symmetric enhancement of the visual quality of MRI brain images, we then apply a symmetrical residual structure. By replacing the convolutional modules in both the encoder and decoder sections with deep residual modules, we establish a balance that counters the vanishing gradient problem commonly faced when the network depth increases. Following this, a symmetrical threefold attention block is integrated. This addition ensures a balanced fusion of local and global image features, fine-tuning the network to symmetrically discern and learn essential image characteristics. This harmonious integration remarkably amplifies the network’s precision in segmenting MRI brain tumors. We further validate the equilibrium achieved by our proposed model using three brain tumor segmentation datasets and four metrics and by juxtaposing our model against 21 traditional and learning-based counterparts. The results confirm that our balanced approach significantly elevates performance in the segmentation of MRI brain tumor images without an asymmetrical increase in computational time. Full article
(This article belongs to the Special Issue Image Processing and Symmetry: Topics and Applications)
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18 pages, 4145 KiB  
Article
Research on Spider Recognition Technology Based on Transfer Learning and Attention Mechanism
by Jianming Wang, Qiyu Chen and Chenyang Shi
Symmetry 2023, 15(9), 1727; https://doi.org/10.3390/sym15091727 - 08 Sep 2023
Cited by 2 | Viewed by 712
Abstract
Methods such as transfer learning and attention mechanisms play an important role in small-sample image classification tasks. However, the conventional transfer method retains too much prior knowledge of the source domain and cannot learn the feature information of the target domain well. At [...] Read more.
Methods such as transfer learning and attention mechanisms play an important role in small-sample image classification tasks. However, the conventional transfer method retains too much prior knowledge of the source domain and cannot learn the feature information of the target domain well. At the same time, it is difficult for the neural network model to find discriminative features and locate key feature regions, and it is easily interfered with by information such as complex backgrounds. Spiders usually appear symmetrical, but they are not perfectly symmetrical. How to accurately classify spider images depends on how to make the model focus on the key features for recognizing spiders in these symmetrical and asymmetrical regions. In view of the above problems, in this paper, we propose ECSM-ResNet-50, a model for small-sample spider image classification. The model fuses channel and spatial information and pays attention to the correlation between different locations in the input data. The Efficient Channel Attention (ECA) mechanism and the spatial attention mechanism were added to the model, and the self-attention mechanism was added to the end of the model. ECSM-ResNet-50 was constructed and trained on a small-sample spider data set (SPIDER9-IMAGE) using a layer-by-layer fine-tuning transfer learning strategy. Compared with ResNet-50, ECSM-ResNet-50 improved the average accuracy of nine species of spider recognition by 1.57% to 90.25%. This study contributes to the field of small-sample image recognition. Full article
(This article belongs to the Special Issue Image Processing and Symmetry: Topics and Applications)
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17 pages, 4002 KiB  
Article
The Methods of Determining Temporal Direction Based on Asymmetric Information of the Optic Disc for Optimal Fovea Detection
by Helmie Arif Wibawa, Raden Sumiharto, Agus Harjoko and Muhammad Bayu Sasongko
Symmetry 2023, 15(9), 1631; https://doi.org/10.3390/sym15091631 - 24 Aug 2023
Viewed by 717
Abstract
Accurate localization of the fovea in fundus images is essential for diagnosing retinal diseases. Existing methods often require extensive data and complex processes to achieve high accuracy, posing challenges for practical implementation. In this paper, we propose an effective and efficient approach for [...] Read more.
Accurate localization of the fovea in fundus images is essential for diagnosing retinal diseases. Existing methods often require extensive data and complex processes to achieve high accuracy, posing challenges for practical implementation. In this paper, we propose an effective and efficient approach for fovea detection using simple image processing operations and a geometric approach based on the optic disc’s position. A key contribution of this study is the successful determination of the temporal direction by leveraging readable asymmetries related to the optic disc and its surroundings. We discuss three methods based on asymmetry conditions, including blood vessel distribution, cup disc inclination, and optic disc location ratio, for detecting the temporal direction. This enables precise determination of the optimal foveal region of interest. Through this optimized fovea region, fovea detection is achieved using straightforward morphological and image processing operations. Extensive testing on popular datasets (DRIVE, DiaretDB1, and Messidor) demonstrates outstanding accuracy of 99.04% and a rapid execution time of 0.251 s per image. The utilization of asymmetrical conditions for temporal direction detection provides a significant advantage, offering high accuracy and efficiency while competing with existing methods. Full article
(This article belongs to the Special Issue Image Processing and Symmetry: Topics and Applications)
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20 pages, 6167 KiB  
Article
An Adaptive Fatigue Detection System Based on 3D CNNs and Ensemble Models
by Ahmed Sedik, Mohamed Marey and Hala Mostafa
Symmetry 2023, 15(6), 1274; https://doi.org/10.3390/sym15061274 - 16 Jun 2023
Cited by 1 | Viewed by 1501
Abstract
Due to the widespread issue of road accidents, researchers have been drawn to investigate strategies to prevent them. One major contributing factor to these accidents is driver fatigue resulting from exhaustion. Various approaches have been explored to address this issue, with machine and [...] Read more.
Due to the widespread issue of road accidents, researchers have been drawn to investigate strategies to prevent them. One major contributing factor to these accidents is driver fatigue resulting from exhaustion. Various approaches have been explored to address this issue, with machine and deep learning proving to be effective in processing images and videos to detect asymmetric signs of fatigue, such as yawning, facial characteristics, and eye closure. This study proposes a multistage system utilizing machine and deep learning techniques. The first stage is designed to detect asymmetric states, including tiredness and non-vigilance as well as yawning. The second stage is focused on detecting eye closure. The machine learning approach employs several algorithms, including Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Multi-layer Perceptron (MLP), Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF). Meanwhile, the deep learning approach utilizes 2D and 3D Convolutional Neural Networks (CNNs). The architectures of proposed deep learning models are designed after several trials, and their parameters have been selected to achieve optimal performance. The effectiveness of the proposed methods is evaluated using video and image datasets, where the video dataset is classified into three states: alert, tired, and non-vigilant, while the image dataset is classified based on four facial symptoms, including open or closed eyes and yawning. A more robust system is achieved by combining the image and video datasets, resulting in multiple classes for detection. Simulation results demonstrate that the 3D CNN proposed in this study outperforms the other methods, with detection accuracies of 99 percent, 99 percent, and 98 percent for the image, video, and mixed datasets, respectively. Notably, this achievement surpasses the highest accuracy of 97 percent found in the literature, suggesting that the proposed methods for detecting drowsiness are indeed effective solutions. Full article
(This article belongs to the Special Issue Image Processing and Symmetry: Topics and Applications)
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19 pages, 15763 KiB  
Article
MCPANet: Multiscale Cross-Position Attention Network for Retinal Vessel Image Segmentation
by Yun Jiang, Jing Liang, Tongtong Cheng, Yuan Zhang, Xin Lin and Jinkun Dong
Symmetry 2022, 14(7), 1357; https://doi.org/10.3390/sym14071357 - 01 Jul 2022
Cited by 5 | Viewed by 1651
Abstract
Accurate medical imaging segmentation of the retinal fundus vasculature is essential to assist physicians in diagnosis and treatment. In recent years, convolutional neural networks (CNN) are widely used to classify retinal blood vessel pixels for retinal blood vessel segmentation tasks. However, the convolutional [...] Read more.
Accurate medical imaging segmentation of the retinal fundus vasculature is essential to assist physicians in diagnosis and treatment. In recent years, convolutional neural networks (CNN) are widely used to classify retinal blood vessel pixels for retinal blood vessel segmentation tasks. However, the convolutional block receptive field is limited, simple multiple superpositions tend to cause information loss, and there are limitations in feature extraction as well as vessel segmentation. To address these problems, this paper proposes a new retinal vessel segmentation network based on U-Net, which is called multi-scale cross-position attention network (MCPANet). MCPANet uses multiple scales of input to compensate for image detail information and applies to skip connections between encoding blocks and decoding blocks to ensure information transfer while effectively reducing noise. We propose a cross-position attention module to link the positional relationships between pixels and obtain global contextual information, which enables the model to segment not only the fine capillaries but also clear vessel edges. At the same time, multiple scale pooling operations are used to expand the receptive field and enhance feature extraction. It further reduces pixel classification errors and eases the segmentation difficulty caused by the asymmetry of fundus blood vessel distribution. We trained and validated our proposed model on three publicly available datasets, DRIVE, CHASE, and STARE, which obtained segmentation accuracy of 97.05%, 97.58%, and 97.68%, and Dice of 83.15%, 81.48%, and 85.05%, respectively. The results demonstrate that the proposed method in this paper achieves better results in terms of performance and segmentation results when compared with existing methods. Full article
(This article belongs to the Special Issue Image Processing and Symmetry: Topics and Applications)
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11 pages, 14791 KiB  
Article
Object Detection by Attention-Guided Feature Fusion Network
by Yuxuan Shi, Yue Fan, Siqi Xu, Yue Gao and Ran Gao
Symmetry 2022, 14(5), 887; https://doi.org/10.3390/sym14050887 - 26 Apr 2022
Cited by 3 | Viewed by 1833
Abstract
One of the most noticeable characteristics of security issues is the prevalence of “Security Asymmetry”. The safety of production and even the lives of workers can be jeopardized if risk factors aren’t detected in time. Today, object detection technology plays a vital role [...] Read more.
One of the most noticeable characteristics of security issues is the prevalence of “Security Asymmetry”. The safety of production and even the lives of workers can be jeopardized if risk factors aren’t detected in time. Today, object detection technology plays a vital role in actual operating conditions. For the sake of warning danger and ensuring the work security, we propose the Attention-guided Feature Fusion Network method and apply it to the Helmet Detection in this paper. AFFN method, which is capable of reliably detecting objects of a wider range of sizes, outperforms previous methods with an mAP value of 85.3% and achieves an excellent result in helmet detection with an mAP value of 62.4%. From objects of finite sizes to a wider range of sizes, the proposed method achieves “symmetry” in the sense of detection. Full article
(This article belongs to the Special Issue Image Processing and Symmetry: Topics and Applications)
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Review

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17 pages, 2021 KiB  
Review
Symmetry in Privacy-Based Healthcare: A Review of Skin Cancer Detection and Classification Using Federated Learning
by Muhammad Mateen Yaqoob, Musleh Alsulami, Muhammad Amir Khan, Deafallah Alsadie, Abdul Khader Jilani Saudagar, Mohammed AlKhathami and Umar Farooq Khattak
Symmetry 2023, 15(7), 1369; https://doi.org/10.3390/sym15071369 - 05 Jul 2023
Cited by 5 | Viewed by 1804
Abstract
Skin cancer represents one of the most lethal and prevalent types of cancer observed in the human population. When diagnosed in its early stages, melanoma, a form of skin cancer, can be effectively treated and cured. Machine learning algorithms play a crucial role [...] Read more.
Skin cancer represents one of the most lethal and prevalent types of cancer observed in the human population. When diagnosed in its early stages, melanoma, a form of skin cancer, can be effectively treated and cured. Machine learning algorithms play a crucial role in facilitating the timely detection of skin cancer and aiding in the accurate diagnosis and appropriate treatment of patients. However, the implementation of traditional machine learning approaches for skin disease diagnosis is impeded by privacy regulations, which necessitate centralized processing of patient data in cloud environments. To overcome the challenges associated with data privacy, federated learning emerges as a promising solution, enabling the development of privacy-aware healthcare systems for skin cancer diagnosis. This paper presents a comprehensive review that examines the obstacles faced by conventional machine learning algorithms and explores the integration of federated learning in the context of privacy-conscious skin cancer prediction healthcare systems. It provides discussion on the various datasets available for skin cancer prediction and provides a performance comparison of various machine learning and federated learning techniques for skin lesion prediction. The objective is to highlight the advantages offered by federated learning and its potential for addressing privacy concerns in the realm of skin cancer diagnosis. Full article
(This article belongs to the Special Issue Image Processing and Symmetry: Topics and Applications)
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