id: 05988497
dt: j
an: 05988497
au: Bardsley, Johnathan M.; Knepper, Sarah; Nagy, James
ti: Structured linear algebra problems in adaptive optics imaging.
so: Adv. Comput. Math. 35, No. 2-4, 103-117 (2011).
py: 2011
pu: Springer, Dordrecht
la: EN
cc: 
ut: adaptive optics; image deblurring; Kronecker product; generalized singular
    value decomposition; wavefront reconstruction; truncated singular value
    decomposition; Tikhonov regularization; LSQR; preconditioning
ci: 
li: doi:10.1007/s10444-011-9172-9
ab: Summary: A main problem in adaptive optics is to reconstruct the phase
    spectrum given noisy phase differences. We present an efficient
    approach to solve the least-squares minimization problem resulting from
    this reconstruction, using either a truncated singular value
    decomposition (TSVD)-type or a Tikhonov-type regularization. Both of
    these approaches make use of Kronecker products and the generalized
    singular value decomposition. The TSVD-type regularization operates as
    a direct method whereas the Tikhonov-type regularization uses a
    preconditioned conjugate gradient type iterative algorithm to achieve
    fast convergence.
rv: