274 Bibliography
[194] Saad, Y. (2003) Itera tive Methods for Sparse Linear Systems,2nd
edition. Philadelphia: SIAM Publications.
[195] Schmidlin, P. (1972) “Iterative separation of sections in tomographic
scintigrams.” Nuklearmedizin, 11, pp. 1–16.
[196] Shepp, L., and Vardi, Y. (1982) “Maximum likelihood reconstruction
for emission tomography.” IEEE Transactions on Medical Imaging,
MI-1, pp. 113–122.
[197] Shieh, M., Byrne, C., and Fiddy, M. (2006) “Image reconstruction:
a unifying model for resolution enhancement and data extrapolation:
Tutorial.” Journal of the Optical Society of America, A, 23(2), pp.
[198] Shieh, M., Byrne, C., Testorf, M., and Fiddy, M. (2006) “Iterative
image reconstruction using prior knowledge.” Journal of the Optical
Society of America, A, 23(6), pp. 1292–1300.
[199] Shieh, M., and Byrne, C. (2006) “Image reconstruction from limited
Fourier data.” Journal of the Optical Society of America, A, 23(11),
pp. 2732–2736.
[200] Stark, H., and Yang, Y. (1998) Vector Space Projections. A Numeri-
cal Approach to Signal a nd Image processing, Neural Nets and Optics.
New York: John Wiley and Sons.
[201] Stark, H., and Woods, J. (2002) Probability and Ra ndom Processes,
with Applications to Signal Pro cessing. Upper Saddle River, NJ:
[202] Tanabe, K. (1971) “Projection method for solving a singular system
of linear equations and its applications.”Numer. Math., 17, pp. 203–
[203] Teboulle, M. (1992) “Entropic proximal mappings with applications
to nonlinear programming.” Mathematics of Op erations Research,
17(3), pp. 670–690.
[204] van der Sluis, A. (1969) “Condition numbers and equilibration of
matrices.” Numer. Math., 14, pp. 14–23.
[205] van der Sluis, A., and van der Vorst, H.A. (1990) “SIRT- and CG-
type methods for the iterative solution of sparse linear least-squares
problems.” Linear Algebra and Its Applications, 130, pp. 257–302.
[206] Vardi, Y., Shepp, L.A. and Kaufman, L. (1985) “A statistical model
for positron emission tomography.”Journal of the American Statistical
Association, 80, pp. 8–20.

Get Iterative Optimization in Inverse Problems now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.