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Special Issue "Image Processing and Symmetry: Topics and Applications"

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

Deadline for manuscript submissions: 31 March 2024 | Viewed by 5870

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Special Issue Editor

Dr. Jun Xu
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.

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
deep learning
lightweight network
super resolution
image denoising
image deblurring

Published Papers (6 papers)

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Research

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Open AccessArticle

Research on Spider Recognition Technology Based on Transfer Learning and Attention Mechanism

Jianming Wang

Qiyu Chen

and

Chenyang Shi

Symmetry 2023, 15(9), 1727; https://doi.org/10.3390/sym15091727 - 08 Sep 2023

Viewed by 217

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 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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Open AccessArticle

The Methods of Determining Temporal Direction Based on Asymmetric Information of the Optic Disc for Optimal Fovea Detection

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 225

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 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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Open AccessArticle

An Adaptive Fatigue Detection System Based on 3D CNNs and Ensemble Models

Ahmed Sedik

Mohamed Marey

and

Hala Mostafa

Symmetry 2023, 15(6), 1274; https://doi.org/10.3390/sym15061274 - 16 Jun 2023

Viewed by 670

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 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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Open AccessArticle

MCPANet: Multiscale Cross-Position Attention Network for Retinal Vessel Image Segmentation

and

Symmetry 2022, 14(7), 1357; https://doi.org/10.3390/sym14071357 - 01 Jul 2022

Cited by 3 | Viewed by 1187

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 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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Open AccessArticle

Object Detection by Attention-Guided Feature Fusion Network

and

Symmetry 2022, 14(5), 887; https://doi.org/10.3390/sym14050887 - 26 Apr 2022

Cited by 2 | Viewed by 1477

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 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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Open AccessReview

Symmetry in Privacy-Based Healthcare: A Review of Skin Cancer Detection and Classification Using Federated Learning

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

Viewed by 787

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 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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Special Issue "Image Processing and Symmetry: Topics and Applications"

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