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Krylov Subspace Methods for Inverse Problems with Application to Image Restoration Mohamed El Guide

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Krylov Subspace Methods for Inverse Problems with Application to Image Restoration Mohamed El Guide Krylov Subspace Methods for Inverse Problems with Application to Image Restoration Mohamed El Guide To cite this version: Mohamed El Guide. Krylov Subspace Methods for Inverse Problems with Application to Image Restoration. Mathematics [math]. Université Cadi Ayyad, Marrakech, 2017. English. tel-01969339 HAL Id: tel-01969339 https://hal.archives-ouvertes.fr/tel-01969339 Submitted on 25 Jan 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Krylov Subspace Methods for Inverse Problems with Application to Image Restoration Mohamed El Guide A DISSERTATION in Mathematics Presented to the Faculties of the University of Cadi Ayyad in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy 26/12/2017 Supervisors of Dissertation Abdeslem Hafid Bentib, Professor of Mathematics Khalide Jbilou, Professor of Mathematics Graduate Group Chairperson Hassane Sadok, Universit´edu Littoral-C^ote-d'Opale Dissertation Committee : Nour Eddine Alaa , Facult´edes Sciences et Techniques de Marrakech Abdeslem Hafid Bentbib, Facult´edes Sciences et Techniques de Marrakech Mohamed El Alaoui Talibi, Facult´edes Sciences Semlalia de Marrakech Said El Hajji, Facult´edes Sciences de Rabat Khalide Jbilou, Universit´edu Littoral-C^ote-d'Opale Abderrahim Messaoudi, ENS Rabat Acknowledgments First of all, I would like to thank the people who believed in me and who allowed me to arrive till the end of this thesis. A special thanks goes to my advisors Abdeslem Hafid Bentib and Khalide Jbilou, for their guidance, support, and encouragement during the period of my preparation to this thesis. I would also like to express my sincere gratitude to Professor Lothar Reichel. He contributed with an immense amount of valuable inputs to improve this dissertation. He always opens the door to reflect his willingness to give advice, discuss research, and share his passion on numerical linear algebra at any time. I would like to express my appreciation to all of my jury members : Pr. Nour Eddine Alaa, Pr. Mohamed El Alaoui Talibi, Pr. Said El Hajji, Pr. Abderrahim Messaoudi and Pr. Hassane Sadok. Special mention goes to Pr. Nour Eddine Alaa, Pr. Abderrahim Messaoudi and Pr. Hassan Sadok who supported me and allowed me to motivated raising, light heart and quiet mind since the beginning of my thesis. I extend my gratitude to all my friends and to all those who have helped in carrying out this work. Finally, the simplest words being the strongest, I address all my affection to my family, and in particular to my mother who made me understand that life is not not only problems that could be solved through mathematical formulas and algorithms. Despite the distance from my family for many years, their intelligence, their confidence, their tenderness, their love carry me and guide ii me all days. Thank you for making me what I am today. I want to thank Abdelghani, Nezha, Kamal, Karim and Zagour who made me redefined the meaning of friendship. iii ABSTRACT Mohamed El Guide Abdeslem Hafid Bentbib, Khalide Jbilou Image restoration often requires the solution of large linear systems of equations with a very ill-conditioned, possibly singular, matrix and an error-contaminated right-hand side. The latter represents the available blur and noise-contaminated image, while the matrix models the blurring. Computation of a meaningful restoration of the available image re- quires the use of a regularization method. We consider the situation when the blurring matrix has a Kronecker product structure and an estimate of the norm of the desired image is available, and illustrate that efficient restoration of the available image can be achieved by Tikhonov regularization based on the global Lanczos method, and by using the connection of the latter to Gauss-type quadrature rules. We also investigate the use of the global Golub-Kahan bidiagonalization method to reduce the given large problem to a small one. The small problem is solved by employing Tikhonov regularization. A re- gularization parameter determines the amount of regularization. The connection between global Golub-Kahan bidiagonalization and Gauss-type quadrature rules is exploited to in- expensively compute bounds that are useful for determining the regularization parameter by the discrepancy principle. We will also present an efficient algorithm for solving the Tikhonov regularization problem of a linear system of equations with multiple right-hand sides contaminated by errors. The proposed algorithm is based on the symmetric block Lanczos algorithm, in connection with block Gauss quadrature rules. We will show how this connection is designed to inexpensively determine a value of the regularization pa- iv rameter when a solution norm constraint is given. Next, we will present four algorithms for the solution of linear discrete ill-posed problems with several right-hand side vectors. These algorithms can be applied, for instance, to multi-channel image restoration when the image degradation model is described by a linear system of equations with multiple right-hand sides that are contaminated by errors. Two of the algorithms are block ge- neralizations of the standard Golub-Kahan bidiagonalization method with the block size equal to the number of channels. One algorithm uses standard Golub-Kahan bidiagonali- zation without restarts for all right-hand sides. These schemes are compared to standard Golub-Kahan bidiagonalization applied to each right-hand side independently. Tikhonov regularization is used to avoid severe error propagation. Applications include the resto- ration of color images are given. We will finally give efficient algorithms to solve total variation (TV) regularization of images contaminated by blur and additive noise. The unconstrained structure of the problem suggests that one can solve a constrained optimi- zation by transforming the original unconstrained minimization problem to an equivalent constrained minimization problem. An augmented Lagrangian method is used to handle the constraints, and an alternating direction method (ADM) is used to iteratively find so- lutions of the subproblems. The solution of these subproblems are belonging to subspaces generated by application of successive orthogonal projections onto a class generalized matrix Krylov subspaces of increasing dimension. v Table des mati`eres 1 Preliminaries 12 1.1 Definitions and theorems . 12 1.2 Kronecker product . 14 1.3 The product [14] . 15 2 A global Lanczos method for Tikhonov regularization minimization problems with a solution norm constraint 17 2.1 Introduction . 17 2.2 The global Lanczos algorithm and Gauss quadrature . 20 2.2.1 The global Lanczos method . 25 2.2.2 Gauss-type rules associated with the global Lanczos method . 28 − + 2.2.3 Evaluation of φk (µ) and φk+1(µ)................... 31 2.3 Computation of an approximate solution of specified norm . 33 2.4 Numerical results . 35 3 Global Golub{Kahan bidiagonalization for Tikhonov regularization mi- nimization problems with error norm constraint 43 3.1 Introduction . 43 vi 3.2 Gauss quadrature for Kronecker structure . 46 3.3 Gauss quadrature . 49 3.4 Parameter selection and computation of an approximate solution . 53 3.5 Numerical examples . 56 4 The symmetric block Lanczos algorithm for linear ill-posed problems 65 4.1 Introduction . 65 4.2 Block Lanczos algorithm, block Gauss quadrature, block anti-Gauss qua- drature and matrix functionals . 69 4.2.1 Partial block Lanczos of the matrix HT H .............. 72 4.2.2 Block Gauss quadrature . 75 4.2.3 Block anti-Gauss quadrature . 77 4.3 Computation of an approximate solution of specified norm . 78 4.4 Numerical results . 83 5 Solution methods for linear discrete ill-posed problems for color image restoration 96 5.1 Introduction . 96 5.2 Solution by partial block Golub{Kahan bidiagonalization . 101 5.3 The GGKB method and Gauss-type quadrature . 107 5.4 Golub{Kahan bidiagonalization for problems with multiple right-hand sides 112 5.5 Numerical results . 116 6 A genaralized Krylov subspace method for TV regularization 125 vii 6.1 Introduction . 125 6.2 TV/L2 minimization problem . 130 6.2.1 Solving the Y-problem . 132 6.2.2 Solving the X-problem . 133 6.2.3 Convergence analysis of TV/L2 problem . 134 6.3 TV/L1 minimization problem . 138 6.3.1 Solving the X-problem . 139 6.3.2 Solving the R-problem . 140 6.3.3 Solving the Y-problem . 140 6.3.4 Convergence analysis of TV/L1 problem . 140 6.4 Generalized matrix Krylov subspace for TV/L1 and TV/L2 regularizations 144 6.5 Numerical results . 148 6.5.1 Example 2 . 151 6.5.2 Example 3 . 153 6.5.3 Example 4 . 155 6.5.4 Example 5 . 157 7 Conclusion 160 viii Table des figures 1 Image satellite. .3 2.1 Example 2 : Approximation X8 of X^ determined by Algorithm 2 for noise level 0.01. 39 2.2 Example 3 : Original image (left), degraded image (center), and restored image (right) for noise of level 0.001. 41 2.3 Example 4 : Original image (left), degraded image (center), and restored image (right) for noise of level 0.001. 42 3.1 Example 3 : Approximation X13 of X^ determined by Algorithm 4 for noise level 0.01. 61 3.2 Example 4 : Original image (left), degraded image (center), and restored image (right) for noise of level 0.001. 63 3.3 Example 5 : Original image (left), degraded image (center), and restored image (right) for noise of level 0.01.
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