Image Fusion

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

1;Front cover;1 2;Image Fusion: Algorithms and Applications;4 3;Copyright page;5 4;Contents;6 5;Preface;14 6;List of contributors;16 7;Chapter 1. Current trends in super-resolution image reconstruction;20 7.1;1.1 Introduction;20 7.2;1.2 Modelling the imaging process;21 7.3;1.3 State-of-the-art SR methods;26 7.4;1.4 A new robust alternative for SR reconstruction;33 7.5;1.5 Comparative evaluations;38 7.6;1.6 Conclusions;40 7.7;Acknowledgements;41 7.8;References;42 8;Chapter 2. Image fusion through multiresolution oversampled decompositions;46 8.1;2.1 Introduction;46 8.2;2.2 Multiresolution analysis;49 8.3;2.3 MTF-tailored multiresolution analysis;59 8.4;2.4 Context-driven multiresolution data fusion;60 8.5;2.5 Quality;67 8.6;2.6 Experimental results;71 8.7;2.7 Concluding remarks;81 8.8;Acknowledgements;82 8.9;References;82 9;Chapter 3. Multisensor and multiresolution image fusion using the linear mixing model;86 9.1;3.1 Introduction;86 9.2;3.2 Data fusion and remote sensing;88 9.3;3.3 The linear mixing model;89 9.4;3.4 Case study;92 9.5;3.5 Conclusions;100 9.6;References;100 10;Chapter 4. Image fusion schemes using ICA bases;104 10.1;4.1 Introduction;104 10.2;4.2 ICA and Topographic ICA bases;107 10.3;4.3 Image fusion using ICA bases;114 10.4;4.4 Pixel-based and region-based fusion rules using ICA bases;115 10.5;4.5 A general optimisation scheme for image fusion ;117 10.6;4.6 Reconstruction of the fused image;121 10.7;4.7 Experiments;124 10.8;4.8 Conclusion;130 10.9;Acknowledgements;134 10.10;References;135 11;Chapter 5. Statistical modelling for wavelet-domain image fusion;138 11.1;5.1 Introduction;138 11.2;5.2 Statistical modelling of multimodal images wavelet coefficients;140 11.3;5.3 Model-based weighted average schemes;144 11.4;5.4 Results;151 11.5;5.5 Conclusions and future work;154 11.6;Acknowledgements;155 11.7;References;155 12;Chapter 6. Theory and implementation of image fusion methods based on the á trous algorithm;158 12.1;6.1 Introduction;158 12.2;6.2 Im
age fusion algorithms;160 12.3;6.3 Results;169 12.4;Acknowledgements;172 12.5;References;172 13;Chapter 7. Bayesian methods for image fusion;176 13.1;7.1 Introduction: fusion using Bayes' theorem;177 13.2;7.2 Direct application of Bayes' theorem to image fusion problems;182 13.3;7.3 Formulation by energy functionals;192 13.4;7.4 Agent based architecture for local Bayesian fusion;204 13.5;7.5 Summary;207 13.6;References;208 14;Chapter 8. Multidimensional fusion by image mosaics;212 14.1;8.1 Introduction;212 14.2;8.2 Panoramic focus;213 14.3;8.3 Panorama with intensity high dynamic range;224 14.4;8.4 Multispectral wide field of view imaging;228 14.5;8.5 Polarisation as well;232 14.6;8.6 Conclusions;234 14.7;Acknowledgements;234 14.8;References;235 15;Chapter 9. Fusion of multispectral and panchromatic images as an optimisation problem;242 15.1;9.1 Introduction;242 15.2;9.2 Image fusion methodologies;244 15.3;9.3 Injection model and optimum parameters computation;247 15.4;9.4 Functional optimisation algorithms;247 15.5;9.5 Quality evaluation criteria;255 15.6;9.6 A fast optimum implementation;257 15.7;9.7 Experimental results and comparisons;258 15.8;9.8 Conclusions;265 15.9;Appendix A. Matlab implementation of the Line Search algorithm in the steepest descent;265 15.10;References;267 16;Chapter 10. Image fusion using optimisation of statistical measurements;270 16.1;10.1 Introduction;270 16.2;10.2 Mathematical preliminaries;271 16.3;10.3 Dispersion Minimisation Fusion (DMF) based methods;272 16.4;10.4 The Kurtosis Maximisation Fusion (KMF) based methods;275 16.5;10.5 Experimental results;280 16.6;10.6 Conclusions;290 16.7;References;290 17;Chapter 11. Fusion of edge maps using statistical approaches;292 17.1;11.1 Introduction;292 17.2;11.2 Operators implemented for this work;294 17.3;11.3 Automatic edge detection ;296 17.4;11.4 Experimental results and discussion;306 17.5;11.5 Conclusions;314 17.6;References;315 18;Chapter 12. Enhancement of multiple sensor images usi
ng joint image fusion and blind restoration;318 18.1;12.1 Introduction;318 18.2;12.2 Robust error estimation theory;320 18.3;12.3 Fusion with error estimation theory;323 18.4;12.4 Joint image fusion and restoration;328 18.5;12.5 Conclusions;343 18.6;Acknowledgement;344 18.7;References;344 19;Chapter 13. Empirical mode decomposition for simultaneous image enhancement and fusion;346 19.1;13.1 Introduction;346 19.2;13.2 EMD and information fusion;347 19.3;13.3 Image denoising;349 19.4;13.4 Texture analysis;353 19.5;13.5 Shade removal;354 19.6;13.6 Fusion of multiple image modalities;356 19.7;13.7 Conclusion;358 19.8;References;358 20;Chapter 14. Region-based multi-focus image fusion;362 20.1;14.1 Introduction;362 20.2;14.2 Region-based multi-focus image fusion in spatial domain;363 20.3;14.3 A spatial domain region-based fusion method using fixed-size blocks;366 20.4;14.4 Fusion using segmented regions;373 20.5;14.5 Discussion;383 20.6;Acknowledgements;383 20.7;References;383 21;Chapter 15. Image fusion techniques for non-destructive testing and remote sensing applications;386 21.1;15.1 Introduction;386 21.2;15.2 The proposed image fusion techniques;388 21.3;15.3 Radar image fusion by MKF;393 21.4;15.4 An NDT/NDE application of FL, PL, and SL;403 21.5;15.5 Conclusions;408 21.6;Acknowledgements;409 21.7;References;410 22;Chapter 16. Concepts of image fusion in remote sensing applications;412 22.1;16.1 Image fusion;412 22.2;16.2 Pan sharpening methods;416 22.3;16.3 Evaluation metrics;432 22.4;16.4 Observations on the MRA-based methods;436 22.5;16.5 Summary;445 22.6;References;446 23;Chapter 17. Pixel-level image fusion metrics;448 23.1;17.1 Introduction;448 23.2;17.2 Signal-level image fusion performance evaluation;450 23.3;17.3 Comparison of image fusion metrics;460 23.4;17.4 Conclusions;467 23.5;References;468 24;Chapter 18. Objectively adaptive image fusion;470 24.1;18.1 Introduction;470 24.2;18.2 Objective fusion evaluation;472 24.3;18.3 Objectively adaptive fusion;4
74 24.4;18.4 Discussion;484 24.5;Acknowledgements;485 24.6;References;485 25;Chapter 19. Performance evaluation of image fusion techniques;488 25.1;19.1 Introduction;488 25.2;19.2 Signal-to-Noise-Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE);490 25.3;19.3 Mutual Information (MI), Fusion Factor (FF), and Fusion Symmetry (FS);492 25.4;19.4 An edge information based objective measure;495 25.5;19.5 Fusion structures;496 25.6;19.6 Fusion of multiple inputs;502 25.7;Acknowledgements;510 25.8;References;511 26;Subject index;512