Image processing plays a crucial role in various fields, including digital multimedia, automated vision detection and inspection, and pattern recognition. AI Deep Learning in Image Processing aims to provide a comprehensive overview of the mechanisms and techniques involved, with a particular focus on the application of advanced AI deep-learning technologies in image processing. The field of image processing has experienced unprecedented growth in recent years. Image processing presents the latest state-of-the-art developments alongside clear explanations of fundamental concepts and contemporary applications. By emphasizing essential principles, this book enables readers to not only implement algorithms and techniques with ease but also identify new challenges and explore innovative applications in the field. New and advanced AI deep-learning techniques for image processing as comparing against traditional image-processing methods Numerous practical examples and AI image-processing-related applications A more intuitive development and clear explanation to the complex technology Updated image-processing technology in medical, chemical, and ecological fields Extensive discussions of performance comparisons of various AI deep-learning image-processing methods This book is designed for students, researchers, and professionals seeking to enhance their knowledge, gain practical insights, and explore the evolving role of image processing in modern technology.

Table of Contents:
PART I Fundamentals of Image Processing 1. Introduction 2. Image Enhancement 3. Mathematical Morphology 4. Image Segmentation 5. Image Representation and Description 6. Feature Extractiion PART II Fundamentals of AI Deep Learning 7. Pattern Recognition 8. Deep Learning 9. Image Processing by Deep Learning 10. Development of Deep-Learning Framework for Mathematical Morphology 11. Deep Morphological Neural Networks PART III Practical Applications 12. A Robust and Blind Image Watermarking System Based on Deep Neural Networks 13. Deep Learning Classification on Optical Coherence Tomography Retina Images 14. Classification of Ecological Data by Deep Learning 15. Joint Learning for Pneumonia Classification and Segmentation on Medical Images 16. Classification of Chest X-Ray Images Using Novel Adaptive Morphological Neural 17. Land-Cover Image Segmentation Based on Individual Class Binary Masks 18. FPA-Net: Frequency-Guided Position-Based Attention Network for Land-Cover Image Segmentation 19. Defense against Adversarial Attacks Based on Stochastic Descent Sign Activation 20. Adaptive Image Reconstruction for Defense against Adversarial Attacks 21. A Novel Multi-Data-Augmentation and Multi-Deep-Learning Framework for Counting Small Vehicles and Crowds 22. Drug Toxicity Prediction by Machine-Learning Approaches 23. An Efficient Detection and Recognition System for Multiple Motorcycle License Plates Based on Decision Tree 24. The Deep Hybrid Neural Network and an Application on Polyp Detection 25. BFC-Cap: Background and Frequency-Guided Contextual Image 26. A Novel Adaptive Data Transformation for Contrastive Learning