Image processing is an integral tool used for research in engineering and the sciences, as well as for diagnostic purposes in medicine. The development of efficient and robust algorithms to reconstruct, enhance, restore, and analyze image data often requires use of sophisticated techniques from linear algebra. The talks in this session will report on recent advances made in image restoration and reconstruction, which are used in a wide range of applications, including astronomical and medical imaging. Important linear algebraic aspects can include exploiting particular matrix structure, computing eigenvalues and singular values of very large matrices, and development and analysis of iterative methods for optimization schemes that incorporate certain regularization techniques.
Thursday 14/07, 16:00–18:00
- Thu 16:00–16:30, Room AV 01.12Iterated Tikhonov Regularization with a General Penalty Term, Lothar Reichel (Kent State University).
- Thu 16:30–17:00, Room AV 01.12Optimal regularized inverse matrices for inverse problems, Julianne Chung (Virginia Tech).
- Thu 17:00–17:30, Room AV 01.12A majorization-minimization generalized Krylov subspace method for lp-lq image restoration, Fiorella Sgallari (University of Bologna).
- Thu 17:30–18:00, Room AV 01.12Enforcing nonnegativity by flexible Krylov subspaces, Silvia Gazzola (University of Bath).
Friday 15/07, 10:30–12:30
- Fri 10:30–11:00, Room AV 01.12Tomographic Image Reconstruction Using Training Images, Per Christian Hansen (Technical University of Denmark).
- Fri 11:00–11:30, Room AV 01.12A convex model for tomographic image reconstruction with uncertainties, Martin Andersen (Technical University of Denmark).
- Fri 11:30–12:00, Room AV 01.12Fast iterative algorithms for wide field of view Adaptive Optics, Daniela Saxenhuber (Johannes Kepler University Linz).
- Fri 12:00–12:30, Room AV 01.12Nonlinear and nonconvex iterative regularization for ill-posed linear problems, Claudio Estatico (University of Genova).