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 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
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Chapter 3 Multisensor and multiresolution image fusion using the linear mixing model
Chapter 4 Image fusion schemes using ICA bases
Chapter 5 Statistical modelling for waveletdomain image fusion
Chapter 6 Theory and implementation of image fusion methods based on the á trous algorithm
Chapter 7 Bayesian methods for image fusion
Chapter 8 Multidimensional fusion by image mosaics
Chapter 12 Enhancement of multiple sensor images using joint image fusion and blind restoration
Chapter 13 Empirical mode decomposition for simultaneous image enhancement and fusion
Chapter 14 Regionbased multifocus image fusion
Chapter 15 Image fusion techniques for nondestructive testing and remote sensing applications
Chapter 16 Concepts of image fusion in remote sensing applications
Chapter 17 Pixellevel image fusion metrics
Chapter 18 Objectively adaptive image fusion
Chapter 19 Performance evaluation of image fusion techniques
analysis applied approximation Bayesian blurring combination common degraded area components Computer Vision correlation corresponding data fusion data set defined denotes detector distribution domain edge detection edge map enhanced Equation ERGAS estimation evaluation filter frame fused image fusion algorithms fusion approach fusion performance fusion process fusion result fusion rules fusion scheme Gaussian global gradient ICA bases IEEE IEEE Transactions Ikonos image fusion image fusion methods Image Processing Information Fusion injection input images Kappa Coefficient kernel Laplacian linear lowpass LRM bands matrix measure metrics minimisation mosaicing multiresolution multiresolution analysis multispectral images noise objective obtained optimisation original image PAN image pan sharpened images panchromatic parameters pixel Proc proposed PSNR QAB/F QuickBird reconstruction Remote Sensing resampled robust samples scale scale ratio scene Section sensor shown in Figure Signal Processing source images spatial detail spatial frequency spatial resolution tion values variables vector visual wavelet transform
Page 22 - ACKNOWLEDGEMENTS This work has been carried out with the support of the Procurement Executive Ministry of Defence, the Science and Engineering Research Council and Metco Ltd..