Blind Image Deconvolution: Theory and Applications

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Overview

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.

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Editorial Reviews

From the Publisher
"Three titles from CRC Press look of interest, though I have not seen the books themselves… P.Campisi and K. Egiazarian have edited a collection of 10 essays on Blind Image Deconvolution, Theory and Applications…"

—P.W. Hawkes in Ultramicroscopy 108 (2008)

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Product Details

  • ISBN-13: 9780849373671
  • Publisher: CRC Press
  • Publication date: 5/28/2007
  • Pages: 472
  • Sales rank: 1,413,396
  • Product dimensions: 6.10 (w) x 9.30 (h) x 1.00 (d)

Table of Contents

BLIND IMAGE DECONVOLUTION: PROBLEM FORMULATION AND EXISTING APPROACHES; Tom E. Bishop, S. Derin Babacan, Bruno Amizic, Aggelos K. Katsaggelos, Tony Chan, and Rafael Molina
Introduction
Mathematical Problem Formulation
Classification of Blind Image Deconvolution Methodologies
Bayesian Framework for Blind Image Deconvolution
Bayesian Modeling of Blind Image Deconvolution
Bayesian Inference Methods in Blind Image Deconvolution
Non-Bayesian Blind Image Deconvolution Models
Conclusions
References
BLIND IMAGE DECONVOLUTION USING BUSSGANG TECHNIQUES: APPLICATIONS TO IMAGE DEBLURRING AND TEXTURE SYNTHESIS; Patrizio Campisi, Alessandro Neri, Stefania Colonnese, Gianpiero Panci, and Gaetano Scarano
Introduction
Bussgang Processes
Single-Channel Bussgang Deconvolution
Multichannel Bussgang deconvolution
Conclusions
References
BLIND MULTIFRAME IMAGE DECONVOLUTION USING ANISOTROPIC SPATIALLY ADAPTIVE FILTERING FOR DENOISING AND REGULARIZATION; Vladimir Katkovnik, Karen Egiazarian, and Jaakko Astola
Introduction
Observation Model and Preliminaries
Frequency Domain Equations
Projection Gradient Optimization
Anisotropic LPA-ICI Spatially Adaptive Filtering
Blind Deconvolution Algorithm
Identifiability and Convergence
Simulations
Conclusions
Acknowledgments
References
BAYESIAN METHODS BASED ON VARIATIONAL APPROXIMATIONS FOR BLIND IMAGE DECONVOLUTION; Aristidis Likas and Nikolas P. Galatsanos
Introduction
Background on Variational Methods
Variational Blind Deconvolution
Numerical Experiments
Conclusions and Future Work
APPENDIX A: Computation of the Variational Bound F(q,?)
APPENDIX B: Maximization of F(q,?)
References
DECONVOLUTION OF MEDICAL IMAGES FROM MICROSCOPIC TO WHOLE BODY IMAGES; Oleg V. Michailovich and Dan R. Adam
Introduction
Nonblind Deconvolution
Blind Deconvolution in Ultrasound Imaging
Blind Deconvolution in SPECT
Blind Deconvolution in Confocal Microscopy
Summary
References
BAYESIAN ESTIMATION OF BLUR AND NOISE IN REMOTE SENSING IMAGING; André Jalobeanu, Josiane Zerubia, and Laure Blanc-Féraud
Introduction
The Forward Model
Bayesian Estimation: Invert the Forward Model
Possible Improvements and Further Development
Results
Conclusions
Acknowledgments
References
DECONVOLUTION AND BLIND DECONVOLUTION IN ASTRONOMY; Eric Pantin, Jean-luc Starck, and Fionn Murtagh
Introduction
The Deconvolution Problem
Linear Regularized Methods
CLEAN
Bayesian Methodology
Iterative Regularized Methods
Wavelet-Based Deconvolution
Deconvolution and Resolution
Myopic and Blind Deconvolution
Conclusions and Chapter Summary
Acknowledgments
References
MULTIFRAME BLIND DECONVOLUTION COUPLED WITH FRAME REGISTRATION AND RESOLUTION ENHANCEMENT; Filip Šroubek, Jan Flusser, and Gabriel Cristóbal
Introduction
Mathematical Model
Polyphase Formulation
Reconstruction of Volatile Blurs
Blind Superresolution
Experiments
Conclusions
Acknowledgments
References
BLIND RECONSTRUCTION OF MULTIFRAME IMAGERY BASED ON FUSION AND CLASSIFICATION; Dimitrios Hatzinakos, Alexia Giannoula, and Jianxin Han
Introduction
System Overview
Recursive Inverse Filtering with Finite Normal-Density Mixtures (RIF-FNM)
Optimal Filter Adaptation
Effects of Noise
The Fusion and Classification Recursive Inverse Filtering Algorithm (FAC-RIF)
Experimental Results
Final Remarks
References
BLIND DECONVOLUTION AND STRUCTURED MATRIX COMPUTATIONS WITH APPLICATIONS TO ARRAY IMAGING; Michael K. Ng and Robert J. Plemmons
Introduction
One-Dimensional Deconvolution Formulation
Regularized and Constrained TLS Formulation
Numerical Algorithms
Two-Dimensional Deconvolution Problems
Numerical Examples
Application: High-Resolution Image Reconstruction
Concluding Remarks and Current Work
Acknowledgments
References
INDEX

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