Hessian-Based Regularization For 3-D Microscopy Image Restoration

We investigate a non quadratic regularizer that is based on the Hessian operator for dealing with the restoration of 3-D images in a variational framework. We show that the regularizer under study is a valid extension of the total-variation (TV) functional, in the sense that it retains its favorable properties while following a similar underlying principle. We argue that the new functional is well suited for the restoration of 3-D biological images since it does not suffer from the well-known staircase effect of TV. Furthermore, we present an efficient 3-D algorithm for the minimization of the corresponding objective function. Finally, we validate the overall proposed regularization framework through image deblurring experiments on simulated and real biological data.


Publié dans:
2012 9Th Ieee International Symposium On Biomedical Imaging (Isbi), 1731-1734
Présenté à:
9th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro, Barcelona, SPAIN, MAY 02-05, 2012
Année
2012
Publisher:
New York, Ieee
ISBN:
978-1-4577-1858-8
Mots-clefs:
Laboratoires:




 Notice créée le 2013-03-28, modifiée le 2018-11-14

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