Infrared Image Processing with Artificial Intelligence: Progress and Challenges

Dear Colleagues,

Infrared (IR) imaging has become an indispensable tool in a wide range of domains, including surveillance, remote sensing, medical diagnostics, autonomous driving, industrial inspection, and environmental monitoring. Unlike visible-spectrum imaging, infrared captures thermal radiation, offering unique advantages in low-light and obscured environments. However, IR image processing poses distinct challenges, such as low contrast, limited texture, noise, and variability in environmental conditions.

Recent advances in artificial intelligence (AI)—particularly deep learning—have led to significant breakthroughs in addressing these challenges. AI-powered methods now play a crucial role in tasks such as image enhancement, object detection and tracking, semantic segmentation, anomaly detection, and multimodal fusion involving IR data. Despite these advances, the field continues to face various obstacles, including limited labeled data, high domain variability, a lack of generalization, and the need for interpretable and energy-efficient models.

This Special Issue aims to compile state-of-the-art research at the intersection of AI and infrared image processing. We welcome the submission of original contributions that explore novel algorithms, datasets, applications, and theoretical frameworks. Both supervised and unsupervised learning paradigms, multimodal approaches, and cross-domain adaptation methods are welcome.

The scope of this Special Issue includes, but is not limited to, the following topics:

• AI-based IR image enhancement and restoration
• Object detection and tracking in IR imagery;
• Semantic segmentation and scene understanding using IR data;
• Deep learning for IR video analysis;
• Domain adaptation and generalization in thermal imaging;
• Multimodal fusion (e.g., IR and visible, IR and LiDAR);
• Small-sample and few-shot learning for IR applications;
• Anomaly detection and predictive maintenance in thermal imagery;
• Explainable and efficient AI models for embedded IR systems;
• Benchmark datasets, simulation tools, and open-source frameworks.

This Special Issue seeks to provide a comprehensive overview of recent progress and future trends in this exciting and rapidly evolving field.

Dr. Ruiheng Zhang
Dr. Na Li
Dr. Yaokun Xu
Dr. Xiaolin Han
Dr. Zhe Cao
Guest Editors

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