  This thesis gives an overview of the state-of-the-art randomized linear
algebra algorithms for singular value decomposition (SVD), including the
presentation of existing pseudo-codes and theoretical error analysis. Our main
focus is on presenting numerical experiments illustrating image restoration
using various randomized singular value decomposition (RSVD) methods;
theoretical error bounds, computed errors, and canonical angles analysis for
these RSVD algorithms.
  This thesis also comes with a newly developed Matlab toolbox that contains
implementations and test examples for some of the state-of-the-art randomized
numerical linear algebra algorithms.
