Image fusion

This book systematically discusses the basic concepts, theories, research and latest trends in image fusion. It focuses on three image fusion categories - pixel, feature and decision - presenting various applications, such as medical imaging, remote sensing, night vision, robotics and autonomous vehicles. Further, it introduces readers to a new category: edge-preserving-based image fusion, and provides an overview of image fusion based on machine learning and deep learning. As such, it is a valuable resource for graduate students and scientists in the field of digital image processing and information fusion.

Intro -- Preface -- Acknowledgments -- Contents -- About the Authors -- Part I: Image Fusion Theories -- Chapter 1: Introduction to Image Fusion -- 1.1 History and Development -- 1.1.1 History -- 1.1.2 Development -- 1.2 Image Fusion Fundamentals -- 1.2.1 Necessity to Combine Information of Images -- 1.2.2 Definition of Image Fusion -- 1.2.3 Image Fusion Objective -- 1.3 Categorization -- 1.4 Fundamental Steps of an Image Fusion System -- 1.5 Types of Image Fusion Systems -- 1.6 Applications -- 1.7 Summary and Outline of the Book -- References -- Chapter 2: Pixel-Level Image Fusion

2.1 Introduction -- 2.1.1 Single-Scale Image Fusion -- 2.1.2 Multi-Scale Image Fusion -- 2.1.2.1 Pyramid-Based Fusion -- 2.1.2.2 Wavelet Transform-Based Fusion -- 2.1.2.3 Filtering-Based Fusion -- 2.2 Pyramid Image Fusion Method Based on Integrated Edge and Texture Information -- 2.2.1 Background -- 2.2.2 Fusion Framework -- 2.2.3 Pyramid Image Fusion of Edge and Texture Information-Specific Steps -- 2.2.4 Beneficial Effects -- 2.3 Image Fusion Method Based on the Expected Maximum and Discrete Wavelet Frames -- 2.3.1 Introduction -- 2.3.2 Discrete Wavelet Frame Multi-Resolution Transform

2.3.3 Basic S…