SVD Approximations for
Large Scale Imaging Problems

Professor James Nagy

SIAM Fellow
Professor, Department of Mathematics and Computer Science,
Emory University


Date: 27 April 2017 (Thursday)
Time: 5:00pm - 6:00pm (Preceded by Reception at 4:30pm)

WLB104, Mrs. Padma Harilela Lecture Theatre,
Lam Woo International Conference Centre, Shaw Campus,
Hong Kong Baptist University



A fundamental tool for analyzing and solving ill-posed inverse problems is the singular value decomposition (SVD). However, in imaging applications the matrices are often too large to be able to efficiently compute the SVD. In this talk we present a general approach to describe how an approximate SVD can be used to efficiently compute approximate solutions for large-scale ill-posed problems, which can then be used either as an initial guess in a nonlinear iterative scheme, or as a preconditioner for linear iterative methods. We show more specifically how to efficiently compute the an SVD approximation for certain applications in image processing.



All are welcome