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Publications about 3D confocal microscopy
Result of the query in the list of publications :
2 Conference articles |
1 - Formulation contrainte pour la déconvolution de bruit de Poisson.
M. Carlavan and L. Blanc-Féraud. In Proc. GRETSI Symposium on Signal and Image Processing, Bordeaux, France, September 2011.
Keywords : 3D confocal microscopy, constrained convex optimization, discrepancy principle, Poisson noise.
@INPROCEEDINGS{CarlavanGRETSI11,
|
| author |
= |
{Carlavan, M. and Blanc-Féraud, L.}, |
| title |
= |
{Formulation contrainte pour la déconvolution de bruit de Poisson}, |
| year |
= |
{2011}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
= |
{Bordeaux, France}, |
| url |
= |
{http://hal.inria.fr/inria-00602015/fr/}, |
| keyword |
= |
{3D confocal microscopy, constrained convex optimization, discrepancy principle, Poisson noise} |
| } |
Résumé :
Nous considérons le problème de la restauration d’image floue et bruitée par du bruit de Poisson. De nombreux travaux ont proposé de traiter ce problème comme la minimisation d’une énergie convexe composée d’un terme d’attache aux données et d’un terme de régularisation choisi selon l’a priori dont on dispose sur l’image à restaurer. Un des problèmes récurrents dans ce type d’approche est le choix du paramètre de régularisation qui contrôle le compromis entre l’attache aux données et la régularisation. Une approche est de choisir ce paramètre de régularisation en procédant à plusieurs minimisations pour plusieurs valeurs du paramètre et en ne gardant que celle qui donne une image restaurée vérifiant un certain critère (qu’il soit qualitatif ou quantitatif). Cette technique est évidemment très couteuse lorsque les données traitées sont de grande dimension, comme c’est le cas en microscopie 3D par exemple. Nous proposons ici de formuler le problème de restauration
d’image floue et bruitée par du bruit de Poisson comme un problème contraint sur l’antilog de la vraisemblance poissonienne et proposons une
estimation de la borne à partir des travaux de Bertero et al. sur le principe de discrepancy pour l’estimation du paramètre de régularisation en présence de bruit de Poisson. Nous montrons des résultats sur des images synthétiques et réelles et comparons avec l'écriture non-contrainte utilisant une approximation gaussienne du bruit de Poisson pour l’estimation du paramètre de régularisation. |
Abstract :
We focus here on the restoration of blurred and Poisson noisy images. Several methods solve this problem by minimizing a convex cost function composed of a data term and a regularizing term chosen from the prior that one have on the image. One of the recurrent problems of this approach is how to choose the regularizing paramater which controls the weight of the regularization term in front of the data term. One method consists in solving the minimization problem for several values of this parameter and by keeping the value which gives an image verifying a quality criterion (either qualitative or quantitative). This technique is obviously time consuming when one deal with high dimensional data such as in 3D microscopy imaging. We propose to formulate the blurred and Poisson noisy images restoration problem as a constrained problem on the antilog of the Poisson likelihood and propose an estimation of the bound from the works of Bertero et al. on the discrepancy principle for the estimation of the regularizing parameter for Poisson noise. We show results on synthetic and real data and we compare these results to the one obtained with the unconstrained formulation using the Gaussian approximation of the Poisson noise for the estimation of the regularizing parameter. |
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2 - A deconvolution method for confocal microscopy with total variation regularization.
N. Dey and L. Blanc-Féraud and C. Zimmer and Z. Kam and J.C. Olivo-Marin and J. Zerubia. In Proc. IEEE International Symposium on Biomedical Imaging (ISBI), Arlington, USA, April 2004.
Keywords : 3D confocal microscopy, Poisson deconvolution, total variation regularization.
@INPROCEEDINGS{Dey04a,
|
| author |
= |
{Dey, N. and Blanc-Féraud, L. and Zimmer, C. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, |
| title |
= |
{A deconvolution method for confocal microscopy with total variation regularization}, |
| year |
= |
{2004}, |
| month |
= |
{April}, |
| booktitle |
= |
{Proc. IEEE International Symposium on Biomedical Imaging (ISBI)}, |
| address |
= |
{Arlington, USA}, |
| pdf |
= |
{http://dx.doi.org/10.1109/ISBI.2004.1398765}, |
| keyword |
= |
{3D confocal microscopy, Poisson deconvolution, total variation regularization} |
| } |
Abstract :
Confocal laser scanning microscopy is a powerful and increasingly popular technique for 3D imaging of biological specimens. However the acquired images are degraded by blur from out-of-focus light and Poisson noise due to photon-limited detection. Several deconvolution methods have been proposed to reduce these degradations, including the Richardson-Lucy algorithm, which computes a maximum likelihood estimation adapted to Poisson statistics. However this method tends to amplify noise if used without regularizing constraint. Here, we propose to combine the Richardson-Lucy algorithm with a regularizing constraint based on total variation, whose smoothing avoids oscillations while preserving edges. We show on simulated images that this constraint improves the deconvolution result both visually and using quantitative measures. |
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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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