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Image fusion algorithms and applications
- Available as
- Online
- Summary
-
The application of sensor technology has brought considerable interest in the area of image fusion. Written by leading experts in the field, this book brings together in one volume the most recent ...
The application of sensor technology has brought considerable interest in the area of image fusion. Written by leading experts in the field, this book brings together in one volume the most recent algorithms, design techniques and applications in the topical field of image fusion. The applications are drawn from military, medical and civilian areas and give practical advice and pointers to the development of future applications in a variety of fields.This book will be an invaluable resource to R&D engineers, academic researchers and system developers requiring the most up-to-da
Details
- Creator
- edited by Tania Stathaki
- Format
- Books
- Language
- English
- Contributors
- Publication
-
- 1st ed
- Amsterdam ; Boston : Elsevier/Academic Press, 2008
- Physical Details
-
- 1 online resource (519 p.)
- ISBNs
- 0080558526, 149330092X, 0123725291, 1281370924, 9780123725295, 9786611370923
- OCLC
- ocn272382270, ocn276360354
- Description based upon print version of record.
- Includes bibliographical references and index.
- English
- Front cover; Image Fusion: Algorithms and Applications; Copyright page; 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; References; 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 fusion2.5 Quality; 2.6 Experimental results; 2.7 Concluding remarks; Acknowledgements; References; 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; References; 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 fusion4.6 Reconstruction of the fused image; 4.7 Experiments; 4.8 Conclusion; Acknowledgements; References; 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; 5.4 Results; 5.5 Conclusions and future work; Acknowledgements; References; Chapter 6. Theory and implementation of image fusion methods based on the รก trous algorithm; 6.1 Introduction; 6.2 Image fusion algorithms; 6.3 Results; Acknowledgements
- ReferencesChapter 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; References; 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; References
- Chapter 9. Fusion of multispectral and panchromatic images as an optimisation problem9.1 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; References; Chapter 10. Image fusion using optimisation of statistical measurements; 10.1 Introduction; 10.2 Mathematical preliminaries
- 10.3 Dispersion Minimisation Fusion (DMF) based methods