Numerical Linear Algebra techniques for Image Restoration and ReconstructionMS34

Image Restoration and Reconstruction are crucial topics that finds application in different fields, such as medicine, engineering, as well as in several scientific fields. Among the different approaches, Numerical Linear Algebra offers various computationally attractive techniques, which can be combined also with sophisticated nonlinear models, exploiting particular matrix structure, working in low dimensional subspaces, estimating efficiently the regularization parameters, and developing iterative methods able to preserve possible constraints on the computed solution.

IR Tools MATLAB Package for Large-Scale Inverse Problems: Introduction to Basic Capabilities
James Nagy (Emory University)
IR Tools MATLAB Package for Large-Scale Inverse Problems: Constrained Krylov Subspace Solvers
Silvia Gazzola (University of Bath)
Multigrid iterative regularization for image deblurring
Marco Donatelli (University of Insubria)
Unmatched Projector/Backprojector Pairs: Perturbation and Convergence Analysis
Per Christian Hansen (Technical University of Denmark)
Point Spread Function Reconstruction and Blind Deconvolution from Adaptive Optics Data of Extremely Large Telescopes
Ronny Ramlau (Kepler University Linz and Johann Radon Institute)
An $\ell^2$-$\ell^q$ regularization method for large discrete ill-posed problems
Alessandro Buccini (Kent State University)
Minimization of the GCV function for Tikhonov Regularization
Caterina Fenu (University of Cagliari)
Improving sharpness in geophysical imaging by TV-based regularization
Giuseppe Rodriguez (University of Cagliari)
Marco Donatelli (University of Insubria)
Caterina Fenu (University of Cagliari)
image reconstruction, image registration, numerical linear algebra