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{ "item_title" : "Artificial Intelligence in Digital Image Processing", "item_author" : [" Hang Chen", "Zhengjun Liu "], "item_description" : "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. ", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/3/03/212/890/3032128900_b.jpg", "price_data" : { "retail_price" : "199.99", "online_price" : "199.99", "our_price" : "199.99", "club_price" : "199.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Artificial Intelligence in Digital Image Processing|Hang Chen

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

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Overview

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.

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Details

  • ISBN-13: 9783032128904
  • ISBN-10: 3032128900
  • Publisher: Springer
  • Publish Date: May 2026
  • Page Count: 276

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