Digital Image Restoration: Springer Series in Information Sciences, cartea 23
Editat de Aggelos K. Katsaggelosen Limba Engleză Paperback – 16 noi 2012
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Specificații
ISBN-13: 9783642635052
ISBN-10: 3642635059
Pagini: 260
Ilustrații: XIV, 243 p.
Dimensiuni: 155 x 235 x 14 mm
Greutate: 0.37 kg
Ediția:Softcover reprint of the original 1st ed. 1991
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Springer Series in Information Sciences
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3642635059
Pagini: 260
Ilustrații: XIV, 243 p.
Dimensiuni: 155 x 235 x 14 mm
Greutate: 0.37 kg
Ediția:Softcover reprint of the original 1st ed. 1991
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Springer Series in Information Sciences
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
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.