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.

Current Trends in Super-Resolution Image Reconstruction; Image Fusion through Multiresolution Oversampled Decompositions; Multisensor and Multiresolution Image Fusion using the Linear Mixing Model; Image Fusion Schemes using ICA Bases; Statistical Modelling for Wavelet-Domain Image Fusion; Theory and Implementation of Image Fusion Methods Based on the Á Trous Algorithm; Bayesian Methods for Image Fusion; Multidimensional Fusion by Image Mosaics; Fusion of Multispectral and Panchromatic Images as an Optimization Problem; Image Fusion using Optimization of Statistical Measurements; Fusion of Edge Maps using Statistical Approaches; Enhancement of Multiple Sensor Images using Joint Image Fusion and Blind Restoration; Empirical Mode Decomposition for Simultaneous Image Enhancement and Fusion; Region Based Multi-Focus Image Fusion; Image Fusion Techniques for Non Destructive Testing and Remote Sensing Applications; Concepts of Image Fusion in Remote Sensing Applications; Pixel-Level Image Fusion Metrics; Optimal Adaptation of Image Fusion using Objective Performance Metrics; Performance Evaluation of Image Fusion Techniques