Blind Image Deconvolution: Theory and Applications, 1st Edition (Hardback) book cover

Blind Image Deconvolution

Theory and Applications, 1st Edition

Edited by Patrizio Campisi, Karen Egiazarian

CRC Press

472 pages | 117 B/W Illus.

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Hardback: 9780849373671
pub: 2007-05-14
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Description

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.

Reviews

"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)

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

Subject Categories

BISAC Subject Codes/Headings:
COM012000
COMPUTERS / Computer Graphics
COM051300
COMPUTERS / Programming / Algorithms
TEC015000
TECHNOLOGY & ENGINEERING / Imaging Systems