Deconvolution and Denoising for Confocal Microscopy - Modeling in Computational Biology and Biomedicine - page 155

4. M.R. Banham and A.K. Katsaggelos. Digital image restoration. IEEE Sig. Proc. Mag. ,
14(2):24-41, March 1997.
5. J.M. Bardsley and J.J. Goldes. Regularization parameter selection methods for ill-posed
poisson maximum likelihood estimation. Inverse Problems , 25(9):095005, 2009.
6. A. Beck and M. Teboulle. A fast iterative shrinkage thresholding algorithm for linear inverse
problems. SIAM J. Imaging Sciences , 2(1):183-202, 2009.
7. D.S.C. Biggs and M. Andrews. Acceleration of iterative image restoration algorithms. Appl.
Opt. , 36(8):1766-1775, 1997.
8. M.J. Booth. Adaptive optics in microscopy. Philos. Transact. A Math. Phys. Eng. Sci. ,
365(1861):2829-2843, December 2007.
9. M.J. Booth, M.A. Neil, R. Juskaitis, and T. Wilson. Adaptive aberration correction in a confocal
microscope. Proc. Natl. Acad. Sci. , 99(9):5788-5792, 2002.
10. M. Born and E. Wolf. Principles of Optics . Cambridge U. Press, 1999.
11. A.C. Bovik, editor. Handbook of image and video processing . Elsevier Academic Press,
Amsterdam [u.a.], 2005.
12. L.M. Bregman. The method of successive projection for finding a common point of convex sets
(Theorems for determining common point of convex sets by method of successive projection).
Soviet Mathematics , 6:688-692, 1965.
13. P. Campisi and K. Egiazarian, editors. Blind Image Deconvolution: Theory and Applications .
CRC Press, 2007.
14. M.B. Cannell, A. McMorland, and C. Soeller. Image enhancement by deconvolution. In J. B.
Pawley, editor, Handbook of Biological Confocal Microscopy , chapter 25, pages 488-500.
Springer, 3 rd edition, 2006.
15. W.A. Carrington, K.E. Fogarty, and F.S. Fay. 3D Fluorescence Imaging of Single Cells Using
Image Restoration. In J. K. Foskett and S. Grinstein, editors, Noninvasive techniques in cell
biology , pages 53-72. Wiley-Liss, 1990.
16. T.F. Chan and J. Shen. Image Processing and Analysis: Variational, PDE, Wavelet, and
Stochastic Methods . SIAM Publisher, 2005.
17. P. Charbonnier, L. Blanc-Feraud, and M. Barlaud. An adaptive reconstruction method involving
discontinuities. In IEEE Int. Conf. Acoust. Speech Signal Process. , volume 5, pages 491-494,
Minneapolis, MN, USA, April 1993.
18. C. Chaux, L. Blanc-Feraud, and J. Zerubia. Wavelet-based restoration methods: application
to 3D confocal microscopy images. In Proc. SPIE , volume 6701, San Diego, USA, August
2007.
19. C. Chaux, J.-C. Pesquet, and N. Pustelnik. Nested iterative algorithms for convex constrained
image recovery problems. SIAM Journal on Imaging Sciences , 2(2):730-762, 2009.
20.P.-C.Cheng,B.-L.Lin,F.-J.Kao,M.Gu,M.-G.Xu,X.Gan,M.-K.Huang,andY.-S.
Wang. Multi-photon fluorescence microscopy - The response of plant cells to high intensity
illumination. Micron , 32:661-670, 2001.
21. J.-A. Conchello and J.W. Lichtman. Optical Sectioning Microscopy. Nature Methods ,

2(12):920-931, 2005.
22. J. Boutet de Monvel, S. Le Calvez, and M. Ulfendahl. Image restoration for confocal
microscopy: improving the limits of deconvolution, with application to the visualization of
the mammalian hearing organ. Biophys. J. , 80(5):2455-2470, 2001.
23. N. Dey, L. Blanc-Feraud, C. Zimmer, Z. Kam, P. Roux, J.-C. Olivo-Marin, and J. Zerubia.
Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope
deconvolution. Microsc. Res. Tech. , 69:260-266, 2006.
24. N. Dey, L. Blanc-Feraud, C. Zimmer, P. Roux, Z. Kam, J.-C. Olivo-Marin, and J. Zerubia. 3D
microscopy deconvolution using richardson-lucy algorithm with total variation regularization.
Research Report 5272, Inria, France, July 2004.
25. A. Diaspro, G. Chirico, C. Usai, P. Romoino, and J. Dobrucki. Photobleaching. In J. B. Pawley,
editor, Handbook of Biological Confocal Microscopy , chapter 39, pages 690-702. Springer, 3 rd
edition, 2006.