Image Fusion Algorithms and Applications 图像融合

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Image Fusion Algorithms and Applications 图像融合 是一本国外比较经典的图像融合算法与应用书籍 很有参考价值
This page intentional lly left blank mage Fusion Algorithms and Applications Edited Tania stathaki Amsterdam· Boston· Heidelberg London· New York Oxford· Paris· San diego e san francisco· Singapore Sydney· Tokyo ELSEVIER Academic Press is an imprint of elsevier Academic Press is an imprint of Elsevier 84 Theobalds road. London wciX 8RR. UK Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands 30 Corporate Drive, Suite 400, Burlington, MA O1803, USA 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA First edition 2008 Copyright o 2008 Elsevier Ltd. All rights reserved No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier's Science Technology Rights Department in Oxford, UK phone:(+44)(0)1865843830fax:(+44)(0)1865853333;email:permissions@elsevier.com.Alternatively youcansubmityourrequestonlinebyvisitingtheElsevierwebsiteathttp:/elsevier.com/locate/permissions, and selecting Obtaining permission to use Elsevier material otice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN:978-0-12-372529-5 British Library Cataloguing- in-Publication Data A catalogue record for this book is available from the British Library For information on all Academic Press publications visitourwebsiteatwww.bookselseviercom Printed and bound in great britain 0809101098765432 rking together to grow libraries in developing countries www.elsevier.comwww.bookaid.orgwww.sabre.org ELSEVIER Internat abre foundation Contents Preface List of contributors 1 Current trends in super-resolution image reconstruction Antonis Katartzis and Maria petrou 1.1 Introduction 1.2 Modelling the imaging process 2 1.2.1 Geometric transformation models 1. 2.2 Image degradation models 1. 2.3 Observation model- Mathematical formulation 1.3 State-of-the-art sr methods 1.3. 1 Frequency domain methods 46779 3.2 Projection Onto Convex Sets(POCs) 1.3.3 Bayesian/variational methods 1.3.4 Interpolation-based approaches 13 1. 4 a new robust alternative for sr reconstruction 14 4.1 Sub-pixel registration 5 1.4.2 Joint bayesian registration/reconstruction 15 1.5 Comparative evaluations 19 1. 6 Conclusions 2 Acknowledgements 22 References 23 2 Image fusion through multiresolution oversampled decompositions 27 Bruno Aiazzi, Stefano Baronti and Massimo selva 2.1 Introduction 27 2.2 Multiresolution analysis 30 2.2.1 Fundamental principles..... 30 2.2.2 Undecimated discrete wavelet transform 33 2.2.3 Multi-level decomposition of wavelet transforms 33 2.2. 4 Translation-invariant wavelet decomposition of a 2-D image 34 2.2.5 trous' wavelet decomposition of an image 36 2.2.6 Laplacian pyramid 38 2.3 MTF-tailored multiresolution analysis 40 2.4 Context-driven multiresolution data fusion ......................................41 2.4.1 Undecimated wavelet-based data fusion scheme 43 2.4.2 Pyramid-based data fusion scheme 44 Contents 2.4.3 'A trous' wavelet data fusion scheme ...................................46 2.4.4 Enhanced Spectral Distortion Minimising(ESDM)model 2.4.5 Enhanced Context-Based(ECB)model 2. 5 Quality 2.5.1 Quality assessment of fusion products 48 2.5.2 Quality indices 50 2.6 Experimental results 52 2.6.1 Data set and compared methods 2.6.2 Performance comparison on Quick Bird data 54 2.6.3 Performance comparison on Ikonos data 57 2. 7 Concluding remarks 62 Acknowledgements 63 References 63 3 Multisensor and multiresolution image fusion using the linear mixing mode Jan gpw clevers and raul zurita-Milla 3.1 Introduction 67 3.2 Data fusion and remote sensing 3.3 The linear mixing model 3.4 Case study 73 3.4.1 Introduction… 73 3.4.2 Study area and data 73 3.4.3 Quality assessment 74 3.4. 4 Results and discussion 3.5 Conclusions 81 References 81 4 Image fusion schemes using ICa bases 85 Nikolaos mitianoudis and tania stathaki 4.1 Introduction 85 4.2 ICA and Topographic ICa bases 88 4.2.1 Definition of bases 88 4.2.2 Training ica bases 4.2.3 Properties of the ICa bases 93 4.3 Image fusion using ica bases 4. 4 Pixel-based and region-based fusion rules using ICa bases 96 4.4.1 A Weight Combination (WC) pixel-based method 97 4.4.2 Region- based image fusion using ica bases.……………….97 4.5 A general optimisation scheme for image fusion 4.5.1 Laplacian priors………… 4.5.2 Verhulstian priors 100 4.6 Reconstruction of the fused image………… ∴102 4.7 Experiments 105 4.7.1 Experiment 1: Artificially distorted images 106 4.7.2 Experiment 2: Out-of-focus image fusion 108 4.7.3 Experiment 3: Multi-modal image fusion 109 Contents 4.8 Conclusion ...................................................................................111 Acknowledgements 115 References 116 5 Statistical modelling for wavelet-domain image fusion 119 Alin Achim, Artur Loza, David bull and nishan Canagarajah 5.1 Introduction 119 5.2 Statistical modelling of multimodal images wavelet coefficients..... 121 5.2. 1 Heavy-tailed distributions 12 5.2.2 Modelling results of wavelet subband coefficients 124 5.3 Model-based weighted average schemes.....…..…..….,125 5.3.1 Saliency estimation using mellin transform 128 5.3.2 Match measure for Sas random variables: The symmetric covariation coefficient 13 5.4 Results 132 5.5 Conclusions and future work 135 Acknowledgements 136 References 136 6 Theory and implementation of image fusion methods based on the a trous gorithm 139 Xavier otazu 6.1 Introduction 139 6.1.1 Multiresolution-based algorithms 140 6.2 Image fusion algorithms 141 6.2. 1 Energy matching 14l 6.2.2 Spatial detail tion the a trous algorith 142 6.2.3 Spatial detail injection 144 6.3 Results 150 Acknowledgements 153 153 7 Bayesian methods for image fusion 157 Virgen Beyerer, Michael Heizmann, Jennifer Sander and loana gheta 7.1 Introduction: fusion using Bayes'theorem............ 158 7.1.1 Why image fusion 158 7.1.2 Three basic requirements for a fusion methodology………….160 7.1.3 Why Bayesian fusion? 162 7.2 Direct application of Bayes'theorem to image fusion problems .....163 7.2.1 Bayesian solution of inverse problems in imaging 163 7.2.2 Bayesian image fusion exemplified for Gaussian distributions . ......165 7.2.3 Bayes estimators 167 7. 2. 4 Multi-stage models 170 7.2.5 Prior modelling.. 171 7.3 Formulation by energy functional 173 7.3.1 Energy terms… 174 7.3.2 Connection with Bayes'methodology via Gibbs'distributions..178 Contents 7.3. 3 Connection with regularisation .180 7.3.4 Energy minimisation 181 7.4 Agent based architecture for local Bayesian fusion 185 7.4.1 Local Bayesian fusion 186 7.4.2 Agent-based architecture 186 7.4.3 The high potential of the proposed conception.. 188 7.5 Summary 188 References 189 8 Multidimensional fusion by image mosaics 193 Yoav y Schechner and shree K. nayar 8.1 Introduction 193 8.2 Panoramic focus 8.2.1 Background on focus ……194 8.2.2 Intentional aberration……… 198 8.2. 3 Data fusion 202 8.3 Panorama with intensity high dynamic range 205 8.3.1 Image acquisition 205 8.3.2 Data fusion… 207 84 Multispectral wide field of view imaging…… 209 8.5 Polarisation as well 213 8.6 Conclusions 215 Acknowledgements 215 References 216 9 Fusion of multispectral and panchromatic images as an optimisation problem 223 Andrea Garzelli, Luca Capobianco and Filippo nencini 9.1 Introduction 223 9.2 Image fusion methodologies 225 9.2.1^ A trous’ wavelet transform∴ ..225 9.2.2 Generalised Intensity-Hue-Saturation transform 227 9.3 Injection model and optimum parameters computation 9.4 Functional optimisation algorithms……….…..…..28 9.4.1U d optimisation 229 9.4.2 Genetic algorithms....................... 233 9.5 Quality evaluation criteria 236 9.52Q4 quality index… 236 9.5.2 Relative dimensionless global error in synthesis 238 9.6 A fast optimum implementation 9.7 Experimental results and comparisons 239 9. 8 Conclusions 246 Appendix a Matlab implementation of the Line search algorithm in the steepest descr 246 References 248 Contents 10 Image fusion using optimisation of statistical measurements 251 Laurent Oudre Tania stathaki and Nikolaos mitianoudis 10.1 Introduction 251 10.2 Mathematical preliminaries 252 10.3 Dispersion Minimisation Fusion(DMf)based methods 10.3.1 The Dispersion Minimisation Fusion method(DMF)…………………255 10.3.2 The Dispersion Minimisation Fusion method With Neighbourhood (DMF WN .256 10.4 The Kurtosis Maximisation Fusion (KMf) based methods 256 10.4.1 The Kurtosis Minimisation Fusion method (KMF) 260 10.4.2 The Robust Kurtosis Minimisation Fusion method (robust KMF) 260 10.5 Experimental results.…… 261 10.5.1 Case one: Multi-focus images. small amount of distortion ...........262 10.5.2 Case two: Multi-focus images. severe distortion 265 10.5. 3 Case three: Multi-sensor images 268 10.6 Conclusions 271 References 271 11 Fusion of edge maps using statistical approaches Stamatia giannarou and Tania stathaki 11.1 Introduction 273 11.2 Operators implemented for this work.... 275 11. 3 Automatic edge detection 277 11.3.1 ROC analysis 277 11.3.2 Weighted kappa coefficient 28 11.3.3 Geometric approach for the Weighted Kappa Coefficient……….284 11.3. 4 An alternative to the selection of the r parameter value .................284 11. 4 Experimental results and discussion 287 11.5 Conclusions.............................. 295 References 296 12 Enhancement of multiple sensor images using joint image fusion and blind restoration Nikolaos mitianoudis and Tania stathaki 12. 1 Introduction 29 12.2 Robust error estimation theory 301 12.2.1 Isotropic diffusion 302 12.2.2 Isotropic diffusion with edge enhancement 303 12.3 Fusion with error estimation theory ................... 304 12.3.1 A novel fusion formulation based on error estimation theory... 305 12.3.2 Fusion experiments of out-of-focus and multimodal image sets using error estimation theory .306 12. 4 Joint image fusion and restoration 12.4.1 Identifying common degraded areas in the sensor images 310 2.4.2 Image restoration 12.4.3 Combining image fusion and restoration 315 12. 4. 4 Examples of joint image fusion and restoration 316

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