Image Processing Based on Convolution Neural Network, 3rd Edition
A Special Issue of Electronics (ISSN 2079-9292) belonging to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 15 November 2026 | Viewed by 580
Editors
Interests: multimedia security; image recognition
Special Issues, Collections and Topics in MDPI journals
Interests: computer vision; information security
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The advent of Convolutional Neural Networks (CNNs) has revolutionized the realm of image processing, leading to breakthroughs in numerous fields such as facial recognition, autonomous vehicles, and medical imaging. This can be attributed to their capacity for processing large-scale image data both efficiently and dependably. While CNN-based image processing techniques have played a significant role in feature extraction, information fusion, and the processing of static, dynamic, color, and grayscale images, it still holds immense potential for future advancements. Currently, more research is applying CNN-based image processing techniques to fields such as medical imaging, biometric identification, entertainment media, and public safety, presenting a variety of more refined and novel visual capabilities to individuals while simultaneously ensuring greater convenience.
Nevertheless, key challenges arise when CNNs are applied in image processing. These include difficulties handling complex and large-scale data, as well as the model's sensitivity to geometric transformations such as image deformation and rotation, which can lead to unstable prediction outcomes. In addition, the black-box nature of CNNs obscures the decision-making process, making it difficult to understand and interpret. Moreover, CNNs require a vast amount of annotated data for training, which can be challenging to obtain in certain fields like medical image processing, thereby limiting their application in these areas. Finally, just as federated learning has enhanced data security in computing networks, similar concerns and solutions are applicable to image processing using CNNs.
This Special Issue aims to provide a platform for researchers to present innovative and effective image processing technologies based on CNNs. This includes addressing the following specific topics:
- Advancements in CNN-based image processing techniques;
- Integration of CNNs with other AI techniques for image processing;
- CNN architecture optimization for image processing;
- Mathematical models for CNN-based image processing;
- Security and privacy in image processing;
- Resource allocation optimization for CNNs in image processing tasks;
- Modeling, analysis, and measurement of computational and requirements for CNN-based image processing;
- Interpretable image processing with CNNs.
Prof. Dr. Shaozhang Niu
Dr. Jiwei Zhang
Guest Editors
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Keywords
- artificial intelligence
- convolutional neural networks
- deep learning
- image processing
- machine learning
- information security
- privacy-preserving
- architecture optimization
- multimedia
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