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.

Key Features

* 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


Academic and industrial researchers and system developers involved in developing military, medical and civilian applications

Table of Contents

  • 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 algorit


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© 2008
Academic Press
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About the author

Tania Stathaki

Affiliations and Expertise

Imperial College, London