Image Fusion (eBook)

Algorithms and Applications

Tania Stathaki (Autor)

eBook Download: PDF

2011 | 1. Auflage
520 Seiten
Elsevier Science (Verlag)
978-0-08-055852-3 (ISBN)

Infos

The growth in the use of sensor technology has led to the demand for image fusion: signal processing techniques that can combine information received from different sensors into a single composite image in an efficient and reliable manner. This book brings together classical and modern algorithms and design architectures, demonstrating through applications how these can be implemented.

Image Fusion: Algorithms and Applications provides a representative collection of the recent advances in research and development in the field of image fusion, demonstrating both spatial domain and transform domain fusion methods including Bayesian methods, statistical approaches, ICA and wavelet domain techniques. It also includes valuable material on image mosaics, remote sensing applications and performance evaluation.

This book will be an invaluable resource to R&D engineers, academic researchers and system developers requiring the most up-to-date and complete information on image fusion algorithms, design architectures and applications.

* Combines theory and practice to create a unique point of reference
* Contains contributions from leading experts in this rapidly-developing field
* Demonstrates potential uses in military, medical and civilian areas

Inhalt

Front cover 1
Image Fusion: Algorithms and Applications 4
Copyright page 5
Contents 6
Preface 14
List of contributors 16
Chapter 1. Current trends in super-resolution image reconstruction 20
1.1 Introduction 20
1.2 Modelling the imaging process 21
1.3 State-of-the-art SR methods 26
1.4 A new robust alternative for SR reconstruction 33
1.5 Comparative evaluations 38
1.6 Conclusions 40
Acknowledgements 41
References 42
Chapter 2. Image fusion through multiresolution oversampled decompositions 46
2.1 Introduction 46
2.2 Multiresolution analysis 49
2.3 MTF-tailored multiresolution analysis 59
2.4 Context-driven multiresolution data fusion 60
2.5 Quality 67
2.6 Experimental results 71
2.7 Concluding remarks 81
Acknowledgements 82
References 82
Chapter 3. Multisensor and multiresolution image fusion using the linear mixing model 86
3.1 Introduction 86
3.2 Data fusion and remote sensing 88
3.3 The linear mixing model 89
3.4 Case study 92
3.5 Conclusions 100
References 100
Chapter 4. Image fusion schemes using ICA bases 104
4.1 Introduction 104
4.2 ICA and Topographic ICA bases 107
4.3 Image fusion using ICA bases 114
4.4 Pixel-based and region-based fusion rules using ICA bases 115
4.5 A general optimisation scheme for image fusion 117
4.6 Reconstruction of the fused image 121
4.7 Experiments 124
4.8 Conclusion 130
Acknowledgements 134
References 135
Chapter 5. Statistical modelling for wavelet-domain image fusion 138
5.1 Introduction 138
5.2 Statistical modelling of multimodal images wavelet coefficients 140
5.3 Model-based weighted average schemes 144
5.4 Results 151
5.5 Conclusions and future work 154
Acknowledgements 155
References 155
Chapter 6. Theory and implementation of image fusion methods based on the á trous algorithm 158
6.1 Introduction 158
6.2 Image fusion algorithms 160
6.3 Results 169
Acknowledgements 172
References 172
Chapter 7. Bayesian methods for image fusion 176
7.1 Introduction: fusion using Bayes' theorem 177
7.2 Direct application of Bayes' theorem to image fusion problems 182
7.3 Formulation by energy functionals 192
7.4 Agent based architecture for local Bayesian fusion 204
7.5 Summary 207
References 208
Chapter 8. Multidimensional fusion by image mosaics 212
8.1 Introduction 212
8.2 Panoramic focus 213
8.3 Panorama with intensity high dynamic range 224
8.4 Multispectral wide field of view imaging 228
8.5 Polarisation as well 232
8.6 Conclusions 234
Acknowledgements 234
References 235
Chapter 9. Fusion of multispectral and panchromatic images as an optimisation problem 242
9.1 Introduction 242
9.2 Image fusion methodologies 244
9.3 Injection model and optimum parameters computation 247
9.4 Functional optimisation algorithms 247
9.5 Quality evaluation criteria 255
9.6 A fast optimum implementation 257
9.7 Experimental results and comparisons 258
9.8 Conclusions 265
Appendix A. Matlab implementation of the Line Search algorithm in the steepest descent 265
References 267
Chapter 10. Image fusion using optimisation of statistical measurements 270
10.1 Introduction 270
10.2 Mathematical preliminaries 271
10.3 Dispersion Minimisation Fusion (DMF) based methods 272
10.4 The Kurtosis Maximisation Fusion (KMF) based methods 275
10.5 Experimental results 280
10.6 Conclusions 290
References 290
Chapter 11. Fusion of edge maps using statistical approaches 292
11.1 Introduction 292
11.2 Operators implemented for this work 294
11.3 Automatic edge detection 296
11.4 Experimental results and discussion 306
11.5 Conclusions 314
References 315
Chapter 12. Enhancement of multiple sensor images using joint image fusion and blind restoration 318
12.1 Introduction 318
12.2 Robust error estimation theory 320
12.3 Fusion with error estimation theory 323
12.4 Joint image fusion and restoration 328
12.5 Conclusions 343
Acknowledgement 344
References 344
Chapter 13. Empirical mode decomposition for simultaneous image enhancement and fusion 346
13.1 Introduction 346
13.2 EMD and information fusion 347
13.3 Image denoising 349
13.4 Texture analysis 353
13.5 Shade removal 354
13.6 Fusion of multiple image modalities 356
13.7 Conclusion 358
References 358
Chapter 14. Region-based multi-focus image fusion 362
14.1 Introduction 362
14.2 Region-based multi-focus image fusion in spatial domain 363
14.3 A spatial domain region-based fusion method using fixed-size blocks 366
14.4 Fusion using segmented regions 373
14.5 Discussion 383
Acknowledgements 383
References 383
Chapter 15. Image fusion techniques for non-destructive testing and remote sensing applications 386
15.1 Introduction 386
15.2 The proposed image fusion techniques 388
15.3 Radar image fusion by MKF 393
15.4 An NDT/NDE application of FL, PL, and SL 403
15.5 Conclusions 408
Acknowledgements 409
References 410
Chapter 16. Concepts of image fusion in remote sensing applications 412
16.1 Image fusion 412
16.2 Pan sharpening methods 416
16.3 Evaluation metrics 432
16.4 Observations on the MRA-based methods 436
16.5 Summary 445
References 446
Chapter 17. Pixel-level image fusion metrics 448
17.1 Introduction 448
17.2 Signal-level image fusion performance evaluation 450
17.3 Comparison of image fusion metrics 460
17.4 Conclusions 467
References 468
Chapter 18. Objectively adaptive image fusion 470
18.1 Introduction 470
18.2 Objective fusion evaluation 472
18.3 Objectively adaptive fusion 474
18.4 Discussion 484
Acknowledgements 485
References 485
Chapter 19. Performance evaluation of image fusion techniques 488
19.1 Introduction 488
19.2 Signal-to-Noise-Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) 490
19.3 Mutual Information (MI), Fusion Factor (FF), and Fusion Symmetry (FS) 492
19.4 An edge information based objective measure 495
19.5 Fusion structures 496
19.6 Fusion of multiple inputs 502
Acknowledgements 510
References 511
Subject index 512

Produkt-Details

• Erscheint lt. Verlag: 29.8.2011
• Sprache: englisch
• Themenwelt: Informatik ► Grafik / Design ► Digitale Bildverarbeitung
• Themenwelt: Mathematik / Informatik ► Informatik ► Theorie / Studium
• Themenwelt: Naturwissenschaften ► Physik / Astronomie ► Elektrodynamik
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Themenwelt: Technik ► Nachrichtentechnik
• ISBN-10: 0-08-055852-6 / 0080558526
• ISBN-13: 978-0-08-055852-3 / 9780080558523