AI-Driven Frontiers in Digital Image Processing: Latest Advances and Prospects

Dear Colleagues,

Digital Image Processing significantly impacts our ability to handle visual information and has become crucial in the development of technology across varied fields. This area's recent progress is mainly fueled by advancements in artificial intelligence (AI), especially deep learning, driving a shift towards more intelligent image processing systems. These AI-driven systems have improved applications in autonomous vehicles, medical diagnostics, remote sensing and space exploration by enabling sophisticated image analysis for decision-making and research.

The emergence of deep learning has introduced efficient methods that accurately handle complex datasets, supporting better data interpretation. AI integration with image processing enhances our capabilities in various sectors, from environmental monitoring to improving vehicle perception systems and medical diagnostic accuracy.

However, the rise of AI in image processing brings challenges, such as managing large data volumes, ensuring model reliability, and addressing privacy and bias concerns. These issues highlight the need for continuous innovation and a collaborative platform for sharing the latest developments in Digital Image Processing.

To address these needs, the "AI-Driven Frontiers in Digital Image Processing: Recent Advances and Prospects" Special Issue aims to gather and disseminate recent advancements, methodologies, and ideas in the field, with the hope of promoting collaboration among experts to overcome current challenges.

Articles and reviews on image processing are welcome. The specific topics to be addressed include, but are not limited to, the following:

• Semantic segmentation and instance segmentation;
• UAV/UAS/BEV/Satellite 3D reconstruction and semantic perception;
• Multimodal Large Models;
• Multimodal data collaborative analysis and processing;
• Video processing;
• Image processing for Medical Imaging and Deep Space Exploration;
• Benchmark datasets for image processing and large models;
• Semi-/Self-supervised image analysis;
• Few-shot/Zero-shot image processing;
• Image-processing-based applications;
• Multi-source sensing data for urban analytics.

Dr. Guanzhou Chen
Dr. Kun Zhu
Dr. Jinzhou Cao
Guest Editors

Manuscript Submission Information

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• deep learning
• digital image processing
• remote sensing
• medical imaging
• autonomous vehicles
• deep space exploration
• multimodal large models
• 3D reconstruction
• video processing