Image Fusion

Algorithms and Applications

1st Edition - November 15, 2006
Author: Tania Stathaki
Language: English
Paperback ISBN:
9 7 8 - 1 - 4 9 3 3 - 0 0 9 2 - 1
Hardback ISBN:
9 7 8 - 0 - 1 2 - 3 7 2 5 2 9 - 5
eBook ISBN:
9 7 8 - 0 - 0 8 - 0 5 5 8 5 2 - 3

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… Read more

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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.

Preface
List of contributors
Chapter 1: Current trends in super-resolution image reconstruction

1.1 Introduction
1.2 Modelling the imaging process
1.3 State-of-the-art SR methods
1.4 A new robust alternative for SR reconstruction
1.5 Comparative evaluations
1.6 Conclusions
Acknowledgements

Chapter 2: Image fusion through multiresolution oversampled decompositions

2.1 Introduction
2.2 Multiresolution analysis
2.3 MTF-tailored multiresolution analysis
2.4 Context-driven multiresolution data fusion
2.5 Quality
2.6 Experimental results
2.7 Concluding remarks
Acknowledgements

Chapter 3: Multisensor and multiresolution image fusion using the linear mixing model

3.1 Introduction
3.2 Data fusion and remote sensing
3.3 The linear mixing model
3.4 Case study
3.5 Conclusions

Chapter 4: Image fusion schemes using ICA bases

4.1 Introduction
4.2 ICA and Topographic ICA bases
4.3 Image fusion using ICA bases
4.4 Pixel-based and region-based fusion rules using ICA bases
4.5 A general optimisation scheme for image fusion
4.6 Reconstruction of the fused image
4.7 Experiments
4.8 Conclusion
Acknowledgements

Chapter 5: Statistical modelling for wavelet-domain image fusion

5.1 Introduction
5.2 Statistical modelling of multimodal images wavelet coefficients
5.3 Model-based weighted average schemes
5.3.1 Saliency estimation using Mellin transform
5.4 Results
5.5 Conclusions and future work
Acknowledgements

Chapter 6: Theory and implementation of image fusion methods based on the á trous algorithm

6.1 Introduction

6.1.1 Multiresolution-based algorithms

6.2 Image fusion algorithms

6.2.1 Energy matching

6.2.1.1 Histogram equalisation
6.2.1.2 Radiance

6.2.2 Spatial detail extraction. The à trous algorithm
6.2.3 Spatial detail injection

6.2.3.1 Direct injection
6.2.3.2 Intensity-based (LHS)
6.2.3.3 Principal component
6.2.3.4 Propor tional injection
6.2.3.5 SRF-based methods
6.2.3.6 Substitution methods

6.3 Results
Acknowledgements

Chapter 7: Bayesian methods for image fusion

7.1 Introduction: fusion using Bayes’ theorem
7.2 Direct application of Bayes’ theorem to image fusion problems
7.3 Formulation by energy functionals
7.4 Agent based architecture for local Bayesian fusion
7.5 Summary

Chapter 8: Multidimensional fusion by image mosaics

8.1 Introduction
8.2 Panoramic focus
8.3 Panorama with intensity high dynamic range
8.4 Multispectral wide field of view imaging
8.5 Polarisation as well
8.6 Conclusions
Acknowledgements

Chapter 9: Fusion of multispectral and panchromatic images as an optimisation problem

9.1 Introduction
9.2 Image fusion methodologies
9.3 Injection model and optimum parameters computation
9.4 Functional optimisation algorithms
9.5 Quality evaluation criteria
9.6 A fast optimum implementation
9.7 Experimental results and comparisons
9.8 Conclusions
Appendix A. Matlab implementation of the Line Search algorithm in the steepest descent

Chapter 10: Image fusion using optimisation of statistical measurements

10.1 Introduction
10.2 Mathematical preliminaries
10.3 Dispersion Minimisation Fusion (DMF) based methods
10.4 The Kurtosis Maximisation Fusion (KMF) based methods
10.5 Experimental results
10.6 Conclusions

Chapter 11: Fusion of edge maps using statistical approaches

11.1 Introduction
11.2 Operators implemented for this work
11.3 Automatic edge detection
11.4 Experimental results and discussion
11.5 Conclusions

Chapter 12: Enhancement of multiple sensor images using joint image fusion and blind restoration

12.1 Introduction
12.2 Robust error estimation theory
12.3 Fusion with error estimation theory
12.4 Joint image fusion and restoration
12.5 Conclusions
Acknowledgement

Chapter 13: Empirical mode decomposition for simultaneous image enhancement and fusion

13.1 Introduction
13.2 EMD and information fusion
13.3 Image denoising
13.4 Texture analysis
13.5 Shade removal
13.6 Fusion of multiple image modalities
13.7 Conclusion

Chapter 14: Region-based multi-focus image fusion

14.1 Introduction
14.2 Region-based multi-focus image fusion in spatial domain
14.3 A spatial domain region-based fusion method using fixed-size blocks
14.4 Fusion using segmented regions
14.5 Discussion
Acknowledgements

Chapter 15: Image fusion techniques for non-destructive testing and remote sensing applications

15.1 Introduction
15.2 The proposed image fusion techniques
15.2.1 The MKF algorithm: how to merge multiple images at different scales
15.3 Radar image fusion by MKF
15.4 An NDT/NDE application of FL, PL, and SL
15.5 Conclusions
Acknowledgements

Chapter 16: Concepts of image fusion in remote sensing applications

16.1 Image fusion
16.2 Pan sharpening methods
16.3 Evaluation metrics
16.4 Observations on the MRA-based methods
16.5 Summary

Chapter 17: Pixel-level image fusion metrics

17.1 Introduction
17.2 Signal-level image fusion performance evaluation
17.3 Comparison of image fusion metrics
17.4 Conclusions

Chapter 18: Objectively adaptive image fusion

18.1 Introduction
18.2 Objective fusion evaluation
18.3 Objectively adaptive fusion
18.4 Discussion
Acknowledgements

Chapter 19: Performance evaluation of image fusion techniques

19.1 Introduction
19.2 Signal-to-Noise-Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE)
19.3 Mutual Information (MI), Fusion Factor (FF), and Fusion Symmetry (FS)
19.4 An edge information based objective measure
19.5 Fusion structures
19.6 Fusion of multiple inputs
Acknowledgements

Subject index

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health

Image Fusion

Algorithms and Applications

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Tania Stathaki