1st Edition

Blind Image Deconvolution Theory and Applications

Edited By Patrizio Campisi, Karen Egiazarian Copyright 2007
    472 Pages 117 B/W Illustrations
    by CRC Press

    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; 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

    Biography

    Patrizio Campisi, Karen Egiazarian

    "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)