Blind Image Deconvolution: Theory and Applications

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ISBN: 9780849373671 | 0849373670
Cover: Hardcover
Copyright: 5/14/2007

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Blind image deconvolution is constantly receiving increasing attention from the academic as well the industrial world due to both its theoretical and practical implications. The field of blind image deconvolution has several applications in different areas such as image restoration, microscopy, medical imaging, biological imaging, remote sensing, astronomy, nondestructive testing, geophysical prospecting, and many others. Blind Image Deconvolution: Theory and Applications surveys the current state of research and practice as presented by the most recognized experts in the field, thus filling a gap in the available literature on blind image deconvolution.Explore the gamut of blind image deconvolution approaches and algorithms that currently exist and follow the current research trends into the future. This comprehensive treatise discusses Bayesian techniques, single- and multi-channel methods, adaptive and multi-frame techniques, and a host of applications to multimedia processing, astronomy, remote sensing imagery, and medical and biological imaging at the whole-body, small-part, and cellular levels. Everything you need to step into this dynamic field is at your fingertips in this unique, self-contained masterwork.For image enhancement and restoration without a priori information, turn to Blind Image Deconvolution: Theory and Applications for the knowledge and techniques you need to tackle real-world problems.

Blind Image Deconvolution: Problem Formulation and Existing Approaches	p. 1
Introduction	p. 1
Mathematical Problem Formulation	p. 4
Classification of Blind Image Deconvolution Methodologies	p. 5
Bayesian Framework for Blind Image Deconvolution	p. 6
Bayesian Modeling of Blind Image Deconvolution	p. 7
Observation Model	p. 7
Parametric Prior Blur Models	p. 8
Prior Image and Blur Models	p. 9
Hyperprior Models	p. 15
Bayesian Inference Methods in Blind Image Deconvolution	p. 16
Maximum a Posteriori and Maximum Likelihood	p. 16
Minimum Mean Squared Error	p. 18
Marginalizing Hidden Variables	p. 19
Variational Bayesian Approach	p. 21
Sampling Methods	p. 22
Non-Bayesian Blind Image Deconvolution Models	p. 24
Spectral and Cepstral Zero Methods	p. 25
Zero Sheet Separation Algorithms	p. 26
ARMA Parameter Estimation Algorithms	p. 26
Nonparametric Deterministic Constraints Algorithms	p. 27
Nonparametric Algorithms Based on Higher-Order Statistics	p. 29
Total Least Squares (TLS)	p. 29
Learning-Based Algorithms	p. 30
Methods for Spatially Varying Degradation	p. 30
Multichannel Methods	p. 31
Conclusions	p. 32
References	p. 32
Blind Image Deconvolution Using Bussgang Techniques: Applications to Image Deblurring and Texture Synthesis	p. 43
Abstract	p. 43
Introduction	p. 44
Bussgang Processes	p. 46
Single-Channel Bussgang Deconvolution	p. 48
Convergency Issue	p. 51
Application to Texture Synthesis	p. 55
Multichannel Bussgang Deconvolution	p. 61
The Observation Model	p. 62
Multichannel Wiener Filter	p. 63
Multichannel Bussgang Algorithm	p. 64
Application to Image Deblurring: Binary Text and Spiky Images	p. 67
Application to Image Deblurring: Natural Images	p. 73
Conclusions	p. 85
References	p. 88
Blind Multiframe Image Deconvolution Using Anisotropic Spatially Adaptive Filtering for Denoising and Regularization	p. 95
Abstract	p. 95
Introduction	p. 96
Blind and Nonblind Inverse	p. 96
Inverse Regularization	p. 98
Observation Model and Preliminaries	p. 100
Frequency Domain Equations	p. 102
Projection Gradient Optimization	p. 104
Anisotropic LPA-ICI Spatially Adaptive Filtering	p. 108
Motivation	p. 109
Sectorial Neighborhoods	p. 110
Adaptive Window Size	p. 110
LPA-ICI Filtering	p. 111
Blind Deconvolution Algorithm	p. 112
Main Procedure	p. 112
Image Alignment	p. 114
Identifiability and Convergence	p. 114
Perfect Reconstruction	p. 114
Hessian and Identifiability	p. 115
Conditioning and Convergence Rate	p. 117
Simulations	p. 120
Criteria and Algorithm Parameters	p. 120
Illustrative Results	p. 122
Perfect Reconstruction	p. 128
Numerical Results	p. 128
Image Alignment	p. 130
Reconstruction of Color Images	p. 132
Conclusions	p. 134
Acknowledgments	p. 136
References	p. 136
Bayesian Methods Based on Variational Approximations for Blind Image Deconvolution	p. 141
Abstract	p. 141
Introduction	p. 142
Background on Variational Methods	p. 145
Variational Blind Deconvolution	p. 146
Variational Functional F(q, ?)	p. 146
Maximization of the Variational Bound F(q, ?)	p. 149
Numerical Experiments	p. 152
Partially Known Case	p. 153
Unknown Case	p. 153
Conclusions and Future Work	p. 154
Computation of the Variational Bound F(q, ?)	p. 155
Maximization of F{q, ?)	p. 157
References	p. 165
Deconvolution of Medical Images from Microscopic to Whole Body Images	p. 169
Abstract	p. 169
Introduction	p. 170
Medical Imaging: Tendencies and Goals	p. 170
Linear Modeling of Image Formation	p. 171
Blind Deconvolution in Medical Ultrasound Imaging	p. 172
Blind Deconvolution in Single Photon Emission Computed Tomography	p. 175
Blind Deconvolution in Confocal Microscopy	p. 176
Organization of the Chapter	p. 178
Nonblind Deconvolution	p. 179
Regularization via Maximum a Posteriori Estimation	p. 179
Numerical Optimization via Newton Method	p. 182
Blind Deconvolution with Shift-Variant Blurs	p. 183
Blind Deconvolution in Ultrasound Imaging	p. 184
Blind Deconvolution via Statistical Modeling	p. 185
Blind Deconvolution via Higher-Order Spectra Analysis	p. 190
Horaomorphic Deconvolution: 1-D Case	p. 192
Homomorphic Deconvolution: 2-D Case	p. 199
Generalized Homomorphic Deconvolution	p. 205
Blind Deconvolution via Inverse Filtering	p. 211
Blind Deconvolution in Spect	p. 215
Origins of the Blurring Artifact in Spect	p. 215
Blind Deconvolution via Alternative Minimization	p. 217
Blind Deconvolution via Nonnegativity and Support Constrains Recursive Inverse Filtering	p. 222
Blind Deconvolution in Confocal Microscopy	p. 223
Maximum Likelihood Deconvolution in Fluorescence Microscopy	p. 223
Refinements of the EM Algorithms	p. 227
Blind Deconvolution in 3-D Transmitted Light Brightfield Microscopy	p. 227
Summary	p. 228
References	p. 230
Bayesian Estimation of Blur and Noise in Remote Sensing Imaging	p. 239
Abstract	p. 239
Introduction	p. 240
Blind Deconvolution: State of the Art	p. 241
Constraining a Difficult Problem	p. 243
The Bayesian Viewpoint	p. 244
The Forward Model	p. 245
Modeling the Natural Scene Using Fractals	p. 245
Understanding the Image Formation	p. 246
Bayesian Estimation: Invert the Forward Model	p. 249
Marginalization and Related Approximations	p. 250
A Natural Parameter Estimation Algorithm (BLINDE)	p. 251
Why Use a Simplified Model?	p. 253
A Simplified, Optimized Algorithm	p. 255
Possible Improvements and Further Development	p. 256
Computing Uncertainties	p. 256
Model Assessment and Checking	p. 257
Robustness-Related Improvements	p. 258
Results	p. 259
First Method: Blinde	p. 259
Second method	p. 259
Conclusions	p. 271
Acknowledgments	p. 271
References	p. 271
Deconvolution and Blind Deconvolution in Astronomy	p. 277
Abstract	p. 277
Introduction	p. 278
The Deconvolution Problem	p. 280
Linear Regularized Methods	p. 282
Least Squares Solution	p. 282
Tikhonov Regularization	p. 282
Clean	p. 283
Bayesian Methodology	p. 283
Definition	p. 284
Maximum Likelihood with Gaussian Noise	p. 284
Gaussian Bayes Model	p. 285
Maximum Likelihood with Poisson Noise	p. 285
Maximum a Posteriori with Poisson Noise	p. 286
Maximum Entropy Method	p. 286
Other Regularization Models	p. 287
Iterative Regularized Methods	p. 288
Constraints	p. 288
Jansson-Van Cittert Method	p. 289
Other Iterative Methods	p. 289
Wavelet-Based Deconvolution	p. 290
Introduction	p. 290
Regularization from the Multiresolution Support	p. 292
Multiresolution Clean	p. 295
The Wavelet Constraint	p. 295
Deconvolution and Resolution	p. 300
Myopic and Blind Deconvolution	p. 301
Myopic Deconvolution	p. 303
Blind Deconvolution	p. 305
Conclusions and Chapter Summary	p. 308
Acknowledgments	p. 309
References	p. 309
Multiframe Blind Deconvolution Coupled with FrameRegistration and Resolution Enhancement	p. 317
	p. 317
Introduction	p. 318
Mathematical Model	p. 321
Polyphase Formulation	p. 323
Integer Downsampling Factor	p. 324
Rational Downsampling Factor	p. 325
Reconstruction of Volatile Blurs	p. 326
The MBD Case	p. 327
The RSR Case	p. 327
Blind Superresolution	p. 330
Experiments	p. 333
Simulated Data	p. 333
Real Data	p. 335
Performance Experiments	p. 338
Conclusions	p. 345
Acknowledgment	p. 345
References	p. 346
Blind Reconstruction of Multiframe Imagery Based on Fusion and Classification	p. 349
	p. 349
Introduction	p. 350
System Overview	p. 351
Recursive Inverse Filtering with Finite Normal-Density Mixtures (RIF-FNM)	p. 352
Image Modeling Using Finite Mixture Distributions	p. 352
Pixel Classification	p. 356
ML-Based Image Fusion	p. 357
Optimal Filter Adaptation	p. 358
Effects of Noise	p. 360
The Fusion and Classification Recursive Inverse Filtering Algorithm (FAC-RIF)	p. 361
The Iterative Algorithm	p. 362
Prefiltering and Postfiltering Processing	p. 363
Classification	p. 363
Fusion-Based Classification	p. 364
Fusion of Reconstructed Images	p. 364
Experimental Results	p. 365
Final Remarks	p. 371
References	p. 371
Blind Deconvolution and Structured Matrix Computations with Applications to Array Imaging	p. 377
Abstract	p. 377
Introduction	p. 378
One-Dimensional Deconvolution Formulation	p. 379
Regularized and Constrained TLS Formulation	p. 382
Symmetric Point Spread Functions	p. 386
Numerical Algorithms	p. 388
The Preconditioned Conjugate Gradient Method	p. 390
Cosine Transform-Based Preconditioners	p. 394
Two-Dimensional Deconvolution Problems	p. 396
Numerical Examples	p. 398
Application: High-Resolution Image Reconstruction	p. 400
Mathematical Model	p. 403
Image Reconstruction Formulation	p. 406
Simulation Results	p. 411
Concluding Remarks and Current Work	p. 416
Acknowledgments	p. 418
References	p. 418
Index	p. 423
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Blind Image Deconvolution: Theory and Applications

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