On the regularizing power of multigrid-type algorithms

We consider the deblurring problem of noisy and blurred images in the case of known space invariant point spread functions with four choices of boundary conditions. We combine an algebraic multigrid previously defined ad hoc for structured matrices related to space invariant operators (Toeplitz, circulants, trigonometric matrix algebras, etc.) and the classical geometric multigrid studied in the partial differential equations context. The resulting technique is parameterized in order to have more degrees of freedom: a simple choice of the parameters allows us to devise a quite powerful regularizing method. It defines an iterative regularizing method where the smoother itself has to be an iterative regularizing method (e.g., conjugate gradient, Landweber, conjugate gradient for normal equations, etc.). More precisely, with respect to the smoother, the regularization properties are improved and the total complexity is lower. Furthermore, in several cases, when it is directly applied to the system $A{\bf f}={\bf g}$, the quality of the restored image is comparable with that of all the best known techniques for the normal equations $A^TA{\bf f}=A^T{\bf g}$, but the related convergence is substantially faster. Finally, the associated curves of the relative errors versus the iteration numbers are ``flatter'' with respect to the smoother (the estimation of the stop iteration is less crucial). Therefore, we can choose multigrid procedures which are much more efficient than classical techniques without losing accuracy in the restored image (as often occurs when using preconditioning). Several numerical experiments show the effectiveness of our proposals

Analisi di strutture nella ricostruzione di immagini e monumenti

1. Definizione ed analisi teorica di nuovi operatori di proiezione per metodi multigrid basati solo sulle informazioni algebriche del problema, applicabili sia a discretizzazioni agli elementi finiti, sia a problemi di ricostruzione di immagini ed a matrici di grafo. 2. Definizione e studio di metodi multilivello regolarizzanti per la ricostruzione di immagini sfocate ed affette da rumore, combinando tecniche nonlineari di edge-preserving con operatori di trasferimento di griglia regolarizzanti che preservano la struttura. 3. Applicazione di condizioni al contorno in grado di preservare segnali smooth a tecniche di regolarizzazione accurate e solitamente computazionalmente costose, (e.g., Total Variation (TV), Regularized Total Least Square (RTLS), preconditioned GMRES, etc.), ricorrendo a trasformate discrete veloci di recente sviluppo (generalizzazione di FFT). 4. Studio di metodi impliciti per EDP paraboliche degeneri con applicazioni sia ai modelli di degrado monumentale sia a problemi di ricostruzione di immagini sfocate con termine regolarizzante non lineare. 5. Analisi spettrale di matrici, con struttura nascosta, non Hermitiane associate a simboli a blocchi con applicazioni al precondizionamento di EDP, alla regolarizzazione non lineare, ed a problemi di ricostruzione di segnali o immagini in cui alcuni campionamenti non sono disponibili o in cui le dimensioni del dominio introducono evidenti distorsioni di tipo prospettico

Symmetrization Techniques in Image Deblurring

This paper presents a couple of preconditioning techniques that can be used to enhance the performance of iterative regularization methods applied to image deblurring problems with a variety of point spread functions (PSFs) and boundary conditions. More precisely, we first consider the anti-identity preconditioner, which symmetrizes the coefficient matrix associated to problems with zero boundary conditions, allowing the use of MINRES as a regularization method. When considering more sophisticated boundary conditions and strongly nonsymmetric PSFs, the anti-identity preconditioner improves the performance of GMRES. We then consider both stationary and iteration-dependent regularizing circulant preconditioners that, applied in connection with the anti-identity matrix and both standard and flexible Krylov subspaces, speed up the iterations. A theoretical result about the clustering of the eigenvalues of the preconditioned matrices is proved in a special case. The results of many numerical experiments are reported to show the effectiveness of the new preconditioning techniques, including when considering the deblurring of sparse images

Spectral features of matrix-sequences, GLT, symbol, and application in preconditioning Krylov methods, image deblurring, and multigrid algorithms.

The final purpose of any scientific discipline can be regarded as the solution of real-world problems. With this aim, a mathematical modeling of the considered phenomenon is often compulsory. Closed-form solutions of the arising functional equations are usually not available and numerical discretization techniques are required. In this setting, the discretization of an infinite-dimensional linear equation via some linear approximation method, leads to a sequence of linear systems of increasing dimension whose coefficient matrices could inherit a structure from the continuous problem. For instance, the numerical approximation by local methods of constant or nonconstant coefficients systems of Partial Differential Equations (PDEs) over multidimensional domains, gives rise to multilevel block Toeplitz or to Generalized Locally Toeplitz (GLT) sequences, respectively. In the context of structured matrices, the convergence properties of iterative methods, like multigrid or preconditioned Krylov techniques, are strictly related to the notion of symbol, a function whose role relies in describing the asymptotical distribution of the spectrum. This thesis can be seen as a byproduct of the combined use of powerful tools like symbol, spectral distribution, and GLT, when dealing with the numerical solution of structured linear systems. We approach such an issue both from a theoretical and practical viewpoint. On the one hand, we enlarge some known spectral distribution tools by proving the eigenvalue distribution of matrix-sequences obtained as combination of some algebraic operations on multilevel block Toeplitz matrices. On the other hand, we take advantage of the obtained results for designing efficient preconditioning techniques. Moreover, we focus on the numerical solution of structured linear systems coming from the following applications: image deblurring, fractional diffusion equations, and coupled PDEs. A spectral analysis of the arising structured sequences allows us either to study the convergence and predict the behavior of preconditioned Krylov and multigrid methods applied to the coefficient matrices, or to design effective preconditioners and multigrid solvers for the associated linear systems

Spectral features of matrix-sequences, GLT, symbol, and application in preconditioning Krylov methods, image deblurring, and multigrid algorithms.

The final purpose of any scientific discipline can be regarded as the solution of real-world problems. With this aim, a mathematical modeling of the considered phenomenon is often compulsory. Closed-form solutions of the arising functional equations are usually not available and numerical discretization techniques are required. In this setting, the discretization of an infinite-dimensional linear equation via some linear approximation method, leads to a sequence of linear systems of increasing dimension whose coefficient matrices could inherit a structure from the continuous problem. For instance, the numerical approximation by local methods of constant or nonconstant coefficients systems of Partial Differential Equations (PDEs) over multidimensional domains, gives rise to multilevel block Toeplitz or to Generalized Locally Toeplitz (GLT) sequences, respectively. In the context of structured matrices, the convergence properties of iterative methods, like multigrid or preconditioned Krylov techniques, are strictly related to the notion of symbol, a function whose role relies in describing the asymptotical distribution of the spectrum. This thesis can be seen as a byproduct of the combined use of powerful tools like symbol, spectral distribution, and GLT, when dealing with the numerical solution of structured linear systems. We approach such an issue both from a theoretical and practical viewpoint. On the one hand, we enlarge some known spectral distribution tools by proving the eigenvalue distribution of matrix-sequences obtained as combination of some algebraic operations on multilevel block Toeplitz matrices. On the other hand, we take advantage of the obtained results for designing efficient preconditioning techniques. Moreover, we focus on the numerical solution of structured linear systems coming from the following applications: image deblurring, fractional diffusion equations, and coupled PDEs. A spectral analysis of the arising structured sequences allows us either to study the convergence and predict the behavior of preconditioned Krylov and multigrid methods applied to the coefficient matrices, or to design effective preconditioners and multigrid solvers for the associated linear systems

Semiconvergence and Relaxation Parameters for Projected SIRT Algorithms

We give a detailed study of the semiconvergence behavior of projected nonstationary simultaneous iterative reconstruction technique (SIRT) algorithms, including the projected Landweber algorithm. We also consider the use of a relaxation parameter strategy, proposed recently for the standard algorithms, for controlling the semiconvergence of the projected algorithms. We demonstrate the semiconvergence and the performance of our strategies by examples taken from tomographic imaging.Funding Agencies|Danish Research Council for Technology and Production Sciences|274-07-0065|</p

An inverse scattering procedure in Lebesgue spaces with non-constant exponents

Within the ever-growing field of electromagnetic imaging, inversion procedures are conventionally described in the mathematical framework of Hilbert spaces. Usually, the over-smoothing effects and oscillations that arise using a Hilbert-space formulation make the dielectric reconstruction of targets inaccurate. This problem is strongly reduced by the recent development of inversion techniques in Banach spaces. However, the selection of the Banach space norm parameter is critical for obtaining precise reconstructions, and no exact rules exist for this choice. To overcome this issue, an innovative approach in variable exponent Lebesgue spaces is proposed here, along with a preliminary numerical validation

FAST PRECONDITIONERS FOR TOTAL VARIATION DEBLURRING WITH ANTIREFLECTIVE BOUNDARY CONDITIONS

Natural Science Foundation of Fujian Province of China for Distinguished Young Scholars [2010J06002]; NCET; U. Insubria; MIUR [20083KLJEZ]In recent works several authors have proposed the use of precise boundary conditions (BCs) for blurring models, and they proved that the resulting choice (Neumann or reflective, antireflective) leads to fast algorithms both for deblurring and for detecting the regularization parameters in presence of noise. When considering a symmetric point spread function, the crucial fact is that such BCs are related to fast trigonometric transforms. In this paper we combine the use of precise BCs with the total variation (TV) approach in order to preserve the jumps of the given signal (edges of the given image) as much as possible. We consider a classic fixed point method with a preconditioned Krylov method (usually the conjugate gradient method) for the inner iteration. Based on fast trigonometric transforms, we propose some preconditioning strategies that are suitable for reflective and antireflective BCs. A theoretical analysis motivates the choice of our preconditioners, and an extensive numerical experimentation is reported and critically discussed. Numerical tests show that the TV regularization with antireflective BCs implies not only a reduced analytical error, but also a lower computational cost of the whole restoration procedure over the other BCs