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Publications about complex wavelet regularization
Result of the query in the list of publications :
Technical and Research Report |
1 - Complex wavelet regularization for 3D confocal microscopy deconvolution.
M. Carlavan and L. Blanc-Féraud. Research Report 7366, INRIA, August 2010.
Keywords : 3D confocal microscopy, Deconvolution, complex wavelet regularization, discrepancy principle, Alternating Direction technique.
@TECHREPORT{RR-7366,
|
| author |
= |
{Carlavan, M. and Blanc-Féraud, L.}, |
| title |
= |
{Complex wavelet regularization for 3D confocal microscopy deconvolution}, |
| year |
= |
{2010}, |
| month |
= |
{August}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{7366}, |
| url |
= |
{http://hal.inria.fr/inria-00509447/fr/}, |
| keyword |
= |
{3D confocal microscopy, Deconvolution, complex wavelet regularization, discrepancy principle, Alternating Direction technique} |
| } |
Abstract :
Confocal microscopy is an increasingly popular technique for 3D
imaging of biological specimens which gives images with a very good resolution
(several tenths of micrometers), even though degraded by both blur and Poisson
noise. Deconvolution methods have been proposed to reduce these degradations,
some of them being regularized on a Total Variation prior, which gives
good results in image restoration but does not allow to retrieve the thin details
(including the textures) of the specimens. We rst propose here to use instead
a wavelet prior based on the Dual-Tree Complex Wavelet transform to retrieve
the thin details of the object. As the regularizing prior e ciency also depends
on the choice of its regularizing parameter, we secondly propose a method to
select the regularizing parameter following a discrepancy principle for Poisson
noise. Finally, in order to implement the proposed deconvolution method, we
introduce an algorithm based on the Alternating Direction technique which allows
to avoid inherent stability problems of the Richardson-Lucy multiplicative
algorithm which is widely used in 3D image restoration. We show some results
on real and synthetic data, and compare these results to the ones obtained with
the Total Variation and the Curvelets priors. We also give preliminary results
on a modi cation of the wavelet transform allowing to deal with the anisotropic
sampling of 3D confocal images. |
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