Image fusion : algorithms and applications
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 an...
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| Main Author | |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
London
Academic Press
2008
Elsevier Science & Technology |
| Edition | 1 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9780123725295 0123725291 149330092X 9781493300921 |
Cover
Table of Contents:
- 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 -- 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 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 -- 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 -- 10.4 The Kurtosis Maximisation Fusion (KMF) based methods -- 10.5 Experimental results -- 10.6 Conclusions -- References -- 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 -- References -- 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 -- References -- 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 -- References -- 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 -- References -- Chapter 15. Image fusion techniques for non-destructive testing and remote sensing applications -- 15.1 Introduction -- 15.2 The proposed image fusion techniques -- 15.3 Radar image fusion by MKF -- 15.4 An NDT/NDE application of FL, PL, and SL -- 15.5 Conclusions -- Acknowledgements -- References -- 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 -- References -- 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 -- References -- Chapter 18. Objectively adaptive image fusion -- 18.1 Introduction -- 18.2 Objective fusion evaluation -- 18.3 Objectively adaptive fusion -- 18.4 Discussion -- Acknowledgements -- References -- 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 -- References -- Subject index
- 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 fusion -- 2.5 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 fusion -- 4.6 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 á trous algorithm -- 6.1 Introduction -- 6.2 Image fusion algorithms -- 6.3 Results -- Acknowledgements -- References