Image Fusion, 1st Edition

Algorithms and Applications

 
Image Fusion, 1st Edition,Tania Stathaki,ISBN9780123725295
 
 
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Academic Press

9780123725295

9780080558523

520

244 X 172

A complete resource containing in one volume the latest algorithms, design techniques and applications on the ‘hot’ topic of image fusion

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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

Description

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.

Readership

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

Tania Stathaki

Affiliations and Expertise

Imperial College, London

Image Fusion, 1st Edition

  • 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
 
 
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