Full Description
The field of image restoration is concerned with the estimation of uncorrupted im ages from noisy, blurred ones. These blurs might be caused by optical distortions, object motion during imaging, or atmospheric turbulence. In many scientific and en gineering applications, such as aerial imaging, remote sensing, electron microscopy, and medical imaging, there is active or potential work in image restoration. The purpose of this book is to provide in-depth treatment of some recent ad vances in the field of image restoration. A survey of the field is provided in the introduction. Recent research results are presented, regarding the formulation of the restoration problem as a convex programming problem, the implementation of restoration algorithms using artificial neural networks, the derivation of non stationary image models (compound random fields) and their application to image estimation and restoration, the development of algorithms for the simultaneous image and blur parameter identification and restoration, and the development of algorithms for restoring scanned photographic images. Special attention is directed to issues of numerical implementation. A large number of pictures demonstrate the performance of the restoration approaches. This book provides a clear understanding of the past achievements, a detailed description of the very important recent developments and the limitations of existing approaches, in the rapidly growing field of image restoration. It will be useful both as a reference book for working scientists and engineers and as a supplementary textbook in courses on image processing.
Contents
1. Introduction.- 1.1 The Digital Image Restoration Problem.- 1.2 Degradation Models.- 1.3 Image Models.- 1.4 Ill-Posed Problems and Regularization Approaches.- 1.5 Overview of Image Restoration Approaches.- 1.6 Discussion.- References.- 2. A Dual Approach to Signal Restoration.- 2.1 Background.- 2.2 Application of Convex Programming to Image Restoration.- 2.3 The Dual Approach to Signal Restoration.- 2.4 Numerical Implementation and Results.- 2.5 Cost Functionals for Sequential Restoration.- 2.6 Relationship Between the Original and Modified Entropy and Cross Entropy Functionals.- References.- 3. Hopfield-Type Neural Networks.- 3.1 Overview.- 3.2 Outline of the Chapter.- 3.3 The Hopfield-Type Associative Content Addressable Memory.- 3.4 Image Restoration Using a Hopfield-Type Neural Network.- 3.5 Summary and Conclusion.- 3.A Appendices.- References.- 4. Compound Gauss-Markov Models for Image Processing.- 4.1 Overview.- 4.2 Compound Markov Random Fields.- 4.3 Joint MAP Estimator.- 4.4 Parameter Identification and Simulation Results.- 4.5 Texture Segmentation.- 4.6 Conclusions.- References.- 5. Image Estimation Using 2D Noncausal Gauss-Markov Random Field Models.- 5.1 Preliminaries.- 5.2 Model Representation.- 5.3 Estimation in GMRF Models.- 5.4 Relaxation Algorithms for MAP Estimation.- 5.5 GNC Algorithm for MAP Estimation of Images Modeled by Compound GMRF.- 5.A Appendices.- References.- 6. Maximum Likelihood Identification and Restoration of Images Using the Expectation-Maximization Algorithm.- 6.1 Overview.- 6.2 Image and Blur Models.- 6.3 ML Parameter Identification.- 6.4 ML Identification via the EM Algorithm.- 6.5 The EM Iterations for the ML Estimation of ø.- 6.6 Modified Forms of the Proposed Algorithm.- 6.7 Experimental Results.- 6.8 Conclusions.- 6.AAppendix: Detailed Derivation of Eqs. (6.43-45).- References.- 7. Nonhomogeneous Image Identification and Restoration Procedures.- 7.1 Image Modeling.- 7.2 Kalman-Type Filtering for Restoration.- 7.3 Parameter Identification.- 7.4 Adaptive Image Restoration.- 7.5 Conclusion.- 7.A Appendix: The Kalman Filter I.- References.- 8. Restoration of Scanned Photographic Images.- 8.1 Motivation.- 8.2 Modeling Scanned Blurred Photographic Images.- 8.3 Restoration of Photographic Images: Theory.- 8.4 Restoration of Photographic Images: Practice.- 8.5 Results.- 8.6 Conclusion.- References.- Additional References.
