Abstract
Fluorescence microscopy has become an important tool in biological and medical sciences for imaging thin specimen, even living ones. Due to Out-of-focus blurring and noise the acquired images are degraded and therefore it can be difficult to analyse them. In the last decade many methods have been proposed to restorate these images. One of the most popular methods to restore microscopy images is an iterative Richardson-Lucy Algorithm with Total Variation Regularization. Besides there are some new approaches based on Bregman Iteration to improve the quality of restored images in general. In this paper we formulate a new algorithm for restoring fluorescense microscope images using Bregman Iteration with special attention to the microscopy specific properties. We can proof that the quality of the restored images increases by using the I-divergence and the mean square error criteria.
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References
Lucy LB. An iterative technique for rectification of observed distributions. Astronom J 1974;79(6):745–65.
Dellard F. The expectation maximization algorithm. Technical Report number GIT-GVU-02-20, 2002.
Van Kempen GMP, Van Vliet LJ. The influence of the regularization parameter and the first estimate on the performance of Tikhonov regularized non-linear image restoration algorithms. J Microsc 2000;198(1):63–75.
Dey N, et al. Richardson-Lucy algorithm with total Variation regularization for 3D microscope deconvolution. Microsc Res Tech 2006;69:260–6.
Bregman L: The relaxation model of finding the common points of convex sets and its application to the solution of problems in convex programming. Comput Math Math Phys 1967;7:200–17.
He L, Marquina A, Osher SJ. Blind deconvolution using TV regularization and Bregman iteration. Int J Imaging Syst Tech 2005;15(1):74–83.
Marquina A, Osher SJ. Image Super-Resolution by TV-Regularization. 2007.
Lie J, Nordbotton J. Inverse scale space for nonlinear regularization. J Math Imaging Vis Arch 2007;27(1):41–50.
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© 2008 Springer-Verlag Berlin Heidelberg
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Remmele, S., Seeland, M., Hesser, J. (2008). Fluorescence Microscopy Deconvolution Based on Bregman Iteration and Richardson-Lucy Algorithm with TV Regularization. In: Tolxdorff, T., Braun, J., Deserno, T.M., Horsch, A., Handels, H., Meinzer, HP. (eds) Bildverarbeitung für die Medizin 2008. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78640-5_15
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DOI: https://doi.org/10.1007/978-3-540-78640-5_15
Publisher Name: Springer, Berlin, Heidelberg
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