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|a MAIN
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|a TA1634
|b .X53 2020
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| 100 |
1 |
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|a Xiao, Gang,
|e author.
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1 |
0 |
|a Image fusion /
|c Gang Xiao, Durga Prasad Bavirisetti, Gang Liu, Xingchen Zhang.
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| 264 |
|
1 |
|a Singapore :
|b Springer ;
|c Shanghai, China :
|b Shanghai Jiao Tong University Press,
|c [2020]
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| 300 |
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|a 1 online resource (415 pages)
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|a text
|b txt
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|a Includes bibliographical references.
|
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|a 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.
|
| 588 |
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|a Description based on online resource; title from digital title page (viewed on October 16, 2020).
|
| 505 |
0 |
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|a 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
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| 505 |
8 |
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|a 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
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| 505 |
8 |
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|a 2.3.3 Basic Structure of the New Fusion Scheme -- 2.3.4 Fusion of the Low-Frequency Band Using the EM Algorithm -- 2.3.5 The Selection of the High-Frequency Band Using the Informative Importance Measure -- 2.3.6 Computer Simulation -- 2.3.7 Conclusions -- 2.4 Image Fusion Method Based on Optimal Wavelet Filter Banks -- 2.4.1 Introduction -- 2.4.2 The Generic Multi-Resolution Image Fusion Algorithm -- 2.4.3 Design Criteria of Filter Banks -- 2.4.4 Optimization Design of Filter Bank for Image Fusion -- 2.4.5 Experiments -- 2.4.6 Conclusion
|
| 505 |
8 |
|
|a 2.5 Anisotropic Diffusion-Based Fusion of Infrared and Visible Sensor Images (ADF) -- 2.5.1 Anisotropic Diffusion -- 2.5.2 Anisotropic Diffusion-Based Fusion Method (ADF) -- 2.5.2.1 Extracting Base and Detail Layers -- 2.5.2.2 Detail Layer Fusion Based on KL Transform -- 2.5.2.3 Base Layer Fusion -- 2.5.2.4 Super Position of Final Detail and Base Layers -- 2.5.3 Experimental Setup -- 2.5.3.1 Image Database -- 2.5.3.2 Fusion Metrics -- 2.5.3.3 Methods for Comparison -- 2.5.3.4 Effect of Free Parameters on the ADF Method -- 2.5.4 Results and Analysis -- 2.5.4.1 Qualitative Analysis
|
| 505 |
8 |
|
|a 2.5.4.2 Quantitative Analysis -- 2.5.4.3 Computational Time -- 2.6 Two-Scale Image Fusion of Infrared and Visible Images Using Saliency Detection -- 2.6.1 Two-Scale Image Fusion (TIF) -- 2.6.1.1 Two-Scale Image Decomposition -- 2.6.1.2 Visual Saliency Detection -- 2.6.1.3 Weight Map Construction -- 2.6.1.4 Detail Layer Fusion -- 2.6.1.5 Base Layer Fusion -- 2.6.1.6 Two-Scale Image Reconstruction -- 2.6.1.7 Color Image Fusion -- 2.6.2 Experimental Setup -- 2.6.2.1 Image Database -- 2.6.2.2 Other Methods for Comparison -- 2.6.2.3 Objective Fusion Metrics -- 2.6.2.4 Parameter Analysis
|
| 650 |
|
0 |
|a Optical data processing.
|0 http://id.loc.gov/authorities/subjects/sh85095143
|
| 650 |
|
0 |
|a Multispectral imaging.
|0 http://id.loc.gov/authorities/subjects/sh85088388
|
| 650 |
|
0 |
|a Computer vision.
|0 http://id.loc.gov/authorities/subjects/sh85029549
|
| 650 |
|
7 |
|a Image processing.
|2 bicssc
|
| 650 |
|
7 |
|a Computers
|x Computer Graphics.
|2 bisacsh
|
| 650 |
|
7 |
|a Computer vision.
|2 fast
|0 (OCoLC)fst00872687
|
| 650 |
|
7 |
|a Multispectral imaging.
|2 fast
|0 (OCoLC)fst01029098
|
| 650 |
|
7 |
|a Optical data processing.
|2 fast
|0 (OCoLC)fst01046675
|
| 655 |
|
0 |
|a Electronic books.
|
| 655 |
|
4 |
|a Electronic books.
|
| 700 |
1 |
|
|a Bavirisetti, Durga Prasad,
|e author.
|
| 700 |
1 |
|
|a Liu, Gang,
|e author.
|
| 700 |
1 |
|
|a Zhang, Xingchen,
|e author.
|
| 776 |
0 |
8 |
|i Print version:
|a Xiao, Gang
|t Image Fusion
|d Singapore : Springer Singapore Pte. Limited,c2020
|z 9789811548666
|
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|t Library of Congress classification
|a TA1634 .X53 2020
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|i 12622991
|
