Artificial Intelligence in Digital Image Processing: Theories, Methods, and Applications

This book is a focused, practice-driven resource organized around 10 key thematic sections, blending foundational AI knowledge with cutting-edge digital image processing applications—ideal for bridging theory and real-world use. It avoids generic coverage, instead diving into specialized, high-demand topics like deep learning fundamentals, deepfake technology, adversarial attacks in computer vision, adaptive cryptography, and generative AI-driven SAR-to-optical image translation. As a postgraduate handbook, it aligns perfectly with courses such as “AI Image Processing,” “Advanced Signal Processing,” and “Optical Information Security,” helping students grasp core concepts (e.g., Q-learning for cancer detection-related image segmentation, deep learning-based remote sensing classification) and build practical skills.
Beyond academia, it caters to a broad range of users: researchers and faculty gain insights into novel directions like secure image processing via optical cryptography and automated dataset generation (SciData-Factory), while industry professionals in remote sensing (secure data handling with dynamic optical transforms), cybersecurity (adversarial defense), and medical imaging (AI-aided cancer detection) find actionable solutions for real-world challenges. Self-learners and career changers benefit from its foundational content and coverage of in-demand skills (aligned with certifications like IEEE Signal Processing), and educational institutions or corporate L&D programs (tech, aerospace, healthcare) can adopt it for upskilling. Supplementary online resources—including topic-specific code and lecture slides—add further value, making the book essential for anyone working in AI-driven image processing.

This book is a focused, practice-driven resource organized around 10 key thematic sections, blending foundational AI knowledge with cutting-edge digital image processing applications—ideal for bridging theory and real-world use. It avoids generic coverage, instead diving into specialized, high-demand topics like deep learning fundamentals, deepfake technology, adversarial attacks in computer vision, adaptive cryptography, and generative AI-driven SAR-to-optical image translation. As a postgraduate handbook, it aligns perfectly with courses such as “AI Image Processing,” “Advanced Signal Processing,” and “Optical Information Security,” helping students grasp core concepts (e.g., Q-learning for cancer detection-related image segmentation, deep learning-based remote sensing classification) and build practical skills.
Beyond academia, it caters to a broad range of users: researchers and faculty gain insights into novel directions like secure image processing via optical cryptography and automated dataset generation (SciData-Factory), while industry professionals in remote sensing (secure data handling with dynamic optical transforms), cybersecurity (adversarial defense), and medical imaging (AI-aided cancer detection) find actionable solutions for real-world challenges. Self-learners and career changers benefit from its foundational content and coverage of in-demand skills (aligned with certifications like IEEE Signal Processing), and educational institutions or corporate L&D programs (tech, aerospace, healthcare) can adopt it for upskilling. Supplementary online resources—including topic-specific code and lecture slides—add further value, making the book essential for anyone working in AI-driven image processing.