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Publications de Nicolas Dey
Résultat de la recherche dans la liste des publications :
Article |
1 - Richardson-Lucy Algorithm with Total Variation Regularization for 3D Confocal Microscope Deconvolution.
N. Dey et L. Blanc-Féraud et C. Zimmer et Z. Kam et P. Roux et J.C. Olivo-Marin et J. Zerubia. Microscopy Research Technique, 69: pages 260-266, avril 2006.
Mots-clés : Microscopie confocale, Methodes variationnelles, Variation totale, Deconvolution.
@ARTICLE{dey_mrt_05,
|
| author |
= |
{Dey, N. and Blanc-Féraud, L. and Zimmer, C. and Kam, Z. and Roux, P. and Olivo-Marin, J.C. and Zerubia, J.}, |
| title |
= |
{Richardson-Lucy Algorithm with Total Variation Regularization for 3D Confocal Microscope Deconvolution}, |
| year |
= |
{2006}, |
| month |
= |
{avril}, |
| journal |
= |
{Microscopy Research Technique}, |
| volume |
= |
{69}, |
| pages |
= |
{260-266}, |
| url |
= |
{http://dx.doi.org/10.1002/jemt.20294}, |
| keyword |
= |
{Microscopie confocale, Methodes variationnelles, Variation totale, Deconvolution} |
| } |
Abstract :
Confocal laser scanning microscopy is a powerful and popular technique for 3D imaging of biological specimens. Although confocal microscopy images are much sharper than standard epifluorescence ones, they are still degraded by residual out-of-focus light and by Poisson noise due to photon-limited
detection. Several deconvolution methods have been proposed to reduce these degradations, including the Richardson-Lucy iterative algorithm, which computes a maximum likelihood estimation adapted to Poisson statistics. As this algorithm tends to amplify noise, regularization constraints based on some prior knowledge on the data have to be applied to stabilize the solution. Here, we propose to combine the Richardson-Lucy algorithm with a regularization constraint based on Total Variation, which suppresses unstable oscillations while preserving object edges. We
show on simulated and real images that this constraint improves the deconvolution results as compared to the unregularized Richardson-Lucy algorithm, both visually and quantitatively. |
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2 Articles de conférence |
1 - Deconvolution in confocal microscopy with total variation regularization.
N. Dey et L. Blanc-Féraud et C. Zimmer et Z. Kam et J.C. Olivo-Marin et J. Zerubia. Dans Proc. French-Danish Workshop on Spatial Statistics and Image Analysis in Biology (SSIAB), pages 117--120, mai 2004.
@INPROCEEDINGS{Dey04b,
|
| author |
= |
{Dey, N. and Blanc-Féraud, L. and Zimmer, C. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, |
| title |
= |
{Deconvolution in confocal microscopy with total variation regularization}, |
| year |
= |
{2004}, |
| month |
= |
{mai}, |
| booktitle |
= |
{Proc. French-Danish Workshop on Spatial Statistics and Image Analysis in Biology (SSIAB)}, |
| pages |
= |
{117--120}, |
| url |
= |
{http://www3.jouy.inra.fr/miaj/public/imaste/ssiab2004/program/abw92/}, |
| keyword |
= |
{} |
| } |
|
2 - A deconvolution method for confocal microscopy with total variation regularization.
N. Dey et L. Blanc-Féraud et C. Zimmer et Z. Kam et J.C. Olivo-Marin et J. Zerubia. Dans Proc. IEEE International Symposium on Biomedical Imaging (ISBI), Arlington, USA, avril 2004.
Mots-clés : 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 |
= |
{avril}, |
| 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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Rapport de recherche et Rapport technique |
1 - 3D Microscopy Deconvolution using Richardson-Lucy Algorithm with Total Variation Regularization.
N. Dey et L. Blanc-Féraud et C. Zimmer et P. Roux et Z. Kam et J.C. Olivo-Marin et J. Zerubia. Rapport de Recherche 5272, INRIA, France, juillet 2004.
Mots-clés : Microscopie confocale, Deconvolution, Reponse impulsionnelle, Variation totale.
@TECHREPORT{5272,
|
| author |
= |
{Dey, N. and Blanc-Féraud, L. and Zimmer, C. and Roux, P. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, |
| title |
= |
{3D Microscopy Deconvolution using Richardson-Lucy Algorithm with Total Variation Regularization}, |
| year |
= |
{2004}, |
| month |
= |
{juillet}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5272}, |
| address |
= |
{France}, |
| url |
= |
{http://hal.inria.fr/inria-00070726/fr/}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/70726/filename/RR-5272.pdf}, |
| ps |
= |
{http://hal.inria.fr/docs/00/07/07/26/PS/RR-5272.ps}, |
| keyword |
= |
{Microscopie confocale, Deconvolution, Reponse impulsionnelle, Variation totale} |
| } |
Résumé :
La microscopie confocale (Confocal laser scanning microscopy ou microscopie confocale à balayage laser) est une méthode puissante de plus en plus populaire pour l'imagerie 3D de spécimens biologiques. Malheureusement, les images acquises sont dégradées non seulement par du flou dû à la lumière provenant de zones du spécimen non focalisées, mais aussi par un bruit de Poisson dû à la détection, qui se fait à faible flux de photons. Plusieurs méthodes de déconvolution ont été proposées pour réduire ces dégradations, avec en particulier l'algorithme itératif de Richardson-Lucy, qui calcule un maximum de vraisemblance adapté à une statistique poissonienne. Mais cet algorithme utilisé comme tel ne converge pas nécessairement vers une solution adaptée, car il tend à amplifier le bruit. Si par contre on l'utilise avec une contrainte de régularisation (connaissance a priori sur l'objet que l'on cherche à restaurer, par exemple), Richardson-Lucy régularisé converge toujours vers une solution adaptée, sans amplification du bruit. Nous proposons ici de combiner l'algorithme de Richardson-Lucy avec une contrainte de régularisation basée sur la Variation Totale, dont l'effet d'adoucissement permet d'éviter les oscillations d'intensité tout en préservant les bords des objets. Nous montrons sur des images synthétiques et sur des images réelles que cette contrainte de régularisation améliore les résultats de la déconvolution à la fois qualitativement et quantitativement. Nous comparons plusieurs méthodes de déconvolution bien connues à la méthode que nous proposons, comme Richardson-Lucy standard (pas de régularisation), Richardson-Lucy régularisé avec Tikhonov-Miller, et un algorithme basé sur la descente de gradients (sous l'hypothèse d'un bruit additif gaussien). |
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 iterative algorithm, which computes a maximum likelihood estimation adapted to Poisson statistics. However this algorithm does not necessarily converge to a suitable solution, as it tends to amplify noise. If it is used with a regularizing constraint (some prior knowledge on the data), Richardson-Lucy regularized with a well-chosen constraint, always converges to a suitable solution. Here, we propose to combine the Richardson-Lucy algorithm with a regularizing constraint based on Total Variation, whose smoothing avoids oscillations while preserving object edges. We show on simulated and real images that this constraint improves the deconvolution results both visually and using quantitative measures. We compare several well-known deconvolution methods to the proposed method, such as standard Richardson-Lucy (no regularization), Richardson-Lucy with Tikhonov-Miller regularization, and an additive gradient-based algorithm. |
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