Guest Editors
Dr. Yan Ming
Email: Yan_Ming@cfar.a-star.edu.sg
Affiliation: Centre of Frontier AI Research, Agency of Science, Technology and Research, Singapore, 138632, Singapore
Homepage:
Research Interests: Medical Image Analysis, Natural Language Processing
Dr. Ziyuan Yang
Email: cziyuanyang@gmail.com
Affiliation: College of Computer Science, Sichuan University, Chengdu, 610065, China
Homepage:
Research Interests: Biometrics, federated learning, image restoration
Associate Prof. Juan Tang
Email: tangjn16@gzhu.edu.cn
Affiliation: School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, 510006, China
Homepage:
Research Interests: Artificial Intelligence and Scientific Computing
Dr. Guanhua Qu
Email: quguanhua93@tju.edu.cn
Affiliation: Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
Homepage:
Research Interests: Smart environmental monitoring, Fire spatiotemporal distribution detection
Associate Prof. Lan Wei
Email: lanwei@gxu.edu.cn
Affiliation: School of Computer, Guangxi University, Nanning, China.
Homepage:
Research Interests: Bioinformatics, Medical Image Analysis, and Machine Learning
Summary
Image enhancement is a pivotal field within artificial intelligence, offering transformative potential for applications in sectors like low-quality image, medical imaging and biomedical research. This special issue, “Transforming Image Enhancement with Efficient AI and Large Language Models,” addresses the increasing demand for advanced image processing techniques that are computationally efficient, scalable, and capable of handling complex, domain-specific challenges. Efficient AI models and large language models (LLMs) are reshaping image enhancement, allowing for more precise, adaptive, and interpretable transformations, especially in resource-constrained settings.
The aim of this special issue is to explore innovations that bridge image enhancement with efficient AI techniques and LLMs, emphasizing solutions that drive advancements in medical image analysis, healthcare diagnostics, and broader biomedical applications. By focusing on cutting-edge AI-driven methodologies, this issue seeks to highlight strategies that reduce computational costs, improve model accuracy, and foster novel, real-time insights for critical applications.
The special issue invites submissions on, but not limited to, the following themes:
- AI-enhanced image processing and Applications
- Data Security in Natural Image Enhancement
- Privacy-Preserving Image Enhancement Algorithms
- Efficient deep learning models for image enhancement
- Computational efficiency in high-resolution image analysis
- Real-time and low-latency image enhancement techniques
- Federated Learning for Secure and Efficient Image Enhancement
- Energy-Efficient AI Models for Large-Scale Image Processing
- Efficient Compression and Storage Techniques for Enhanced Images
- Attack Detection and Mitigation in AI-Enhanced Imaging Systems
- Validation and Security Audits for AI-Enhanced Imaging Systems
- Privacy-preserving image processing in medical and biomedical fields
- Domain-specific model adaptation for medical image diagnostics
- Robust Image Enhancement Against Adversarial Attacks
- Efficient Resource Utilization in Large Language Models for Image Processing
- Secure Transfer Learning for Image Enhancement in Biomedical Applications
- Applications of large language models in image interpretation and enhancement
Keywords
Image enhancement, efficient AI, data privacy, AI security
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