Image Fusion: Algorithms and Applications
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Table of contents
- Image Fusion: Algorithms and Applicationsiii
- Copyright pageiv
- Contentsv
- Prefacexiii
- List of contributorsxv
- Chapter 1. Current trends in super-resolution image reconstruction1
- 1.1 Introduction1
- 1.2 Modelling the imaging process2
- 1.3 State-of-the-art SR methods7
- 1.4 A new robust alternative for SR reconstruction14
- 1.5 Comparative evaluations19
- 1.6 Conclusions21
- Acknowledgements22
- References23
- Chapter 2. Image fusion through multiresolution oversampled decompositions27
- 2.1 Introduction27
- 2.2 Multiresolution analysis30
- 2.3 MTF-tailored multiresolution analysis40
- 2.4 Context-driven multiresolution data fusion41
- 2.5 Quality48
- 2.6 Experimental results52
- 2.7 Concluding remarks62
- Acknowledgements63
- References63
- Chapter 3. Multisensor and multiresolution image fusion using the linear mixing model67
- 3.1 Introduction67
- 3.2 Data fusion and remote sensing69
- 3.3 The linear mixing model70
- 3.4 Case study73
- 3.5 Conclusions81
- References81
- Chapter 4. Image fusion schemes using ICA bases85
- 4.1 Introduction85
- 4.2 ICA and Topographic ICA bases88
- 4.3 Image fusion using ICA bases95
- 4.4 Pixel-based and region-based fusion rules using ICA bases96
- 4.5 A general optimisation scheme for image fusion98
- 4.6 Reconstruction of the fused image102
- 4.7 Experiments105
- 4.8 Conclusion111
- Acknowledgements115
- References116
- Chapter 5. Statistical modelling for wavelet-domain image fusion119
- 5.1 Introduction119
- 5.2 Statistical modelling of multimodal images wavelet coefficients121
- 5.3 Model-based weighted average schemes125
- 5.4 Results132
- 5.5 Conclusions and future work135
- Acknowledgements136
- References136
- Chapter 6. Theory and implementation of image fusion methods based on the á trous algorithm139
- 6.1 Introduction139
- 6.2 Image fusion algorithms141
- 6.3 Results150
- Acknowledgements153
- References153
- Chapter 7. Bayesian methods for image fusion157
- 7.1 Introduction: fusion using Bayes' theorem158
- 7.2 Direct application of Bayes' theorem to image fusion problems163
- 7.3 Formulation by energy functionals173
- 7.4 Agent based architecture for local Bayesian fusion185
- 7.5 Summary188
- References189
- Chapter 8. Multidimensional fusion by image mosaics193
- 8.1 Introduction193
- 8.2 Panoramic focus194
- 8.3 Panorama with intensity high dynamic range205
- 8.4 Multispectral wide field of view imaging209
- 8.5 Polarisation as well213
- 8.6 Conclusions215
- Acknowledgements215
- References216
- Chapter 9. Fusion of multispectral and panchromatic images as an optimisation problem223
- 9.1 Introduction223
- 9.2 Image fusion methodologies225
- 9.3 Injection model and optimum parameters computation228
- 9.4 Functional optimisation algorithms228
- 9.5 Quality evaluation criteria236
- 9.6 A fast optimum implementation238
- 9.7 Experimental results and comparisons239
- 9.8 Conclusions246
- Appendix A. Matlab implementation of the Line Search algorithm in the steepest descent246
- References248
- Chapter 10. Image fusion using optimisation of statistical measurements251
- 10.1 Introduction251
- 10.2 Mathematical preliminaries252
- 10.3 Dispersion Minimisation Fusion (DMF) based methods253
- 10.4 The Kurtosis Maximisation Fusion (KMF) based methods256
- 10.5 Experimental results261
- 10.6 Conclusions271
- References271
- Chapter 11. Fusion of edge maps using statistical approaches273
- 11.1 Introduction273
- 11.2 Operators implemented for this work275
- 11.3 Automatic edge detection277
- 11.4 Experimental results and discussion287
- 11.5 Conclusions295
- References296
- Chapter 12. Enhancement of multiple sensor images using joint image fusion and blind restoration299
- 12.1 Introduction299
- 12.2 Robust error estimation theory301
- 12.3 Fusion with error estimation theory304
- 12.4 Joint image fusion and restoration309
- 12.5 Conclusions324
- Acknowledgement325
- References325
- Chapter 13. Empirical mode decomposition for simultaneous image enhancement and fusion327
- 13.1 Introduction327
- 13.2 EMD and information fusion328
- 13.3 Image denoising330
- 13.4 Texture analysis334
- 13.5 Shade removal335
- 13.6 Fusion of multiple image modalities337
- 13.7 Conclusion339
- References339
- Chapter 14. Region-based multi-focus image fusion343
- 14.1 Introduction343
- 14.2 Region-based multi-focus image fusion in spatial domain344
- 14.3 A spatial domain region-based fusion method using fixed-size blocks347
- 14.4 Fusion using segmented regions354
- 14.5 Discussion364
- Acknowledgements364
- References364
- Chapter 15. Image fusion techniques for non-destructive testing and remote sensing applications367
- 15.1 Introduction367
- 15.2 The proposed image fusion techniques369
- 15.3 Radar image fusion by MKF374
- 15.4 An NDT/NDE application of FL, PL, and SL384
- 15.5 Conclusions389
- Acknowledgements390
- References391
- Chapter 16. Concepts of image fusion in remote sensing applications393
- 16.1 Image fusion393
- 16.2 Pan sharpening methods397
- 16.3 Evaluation metrics413
- 16.4 Observations on the MRA-based methods417
- 16.5 Summary426
- References427
- Chapter 17. Pixel-level image fusion metrics429
- 17.1 Introduction429
- 17.2 Signal-level image fusion performance evaluation431
- 17.3 Comparison of image fusion metrics441
- 17.4 Conclusions448
- References449
- Chapter 18. Objectively adaptive image fusion451
- 18.1 Introduction451
- 18.2 Objective fusion evaluation453
- 18.3 Objectively adaptive fusion455
- 18.4 Discussion465
- Acknowledgements466
- References466
- Chapter 19. Performance evaluation of image fusion techniques469
- 19.1 Introduction469
- 19.2 Signal-to-Noise-Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE)471
- 19.3 Mutual Information (MI), Fusion Factor (FF), and Fusion Symmetry (FS)473
- 19.4 An edge information based objective measure476
- 19.5 Fusion structures477
- 19.6 Fusion of multiple inputs483
- Acknowledgements491
- References492
- Subject index493
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
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