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Publications sur Poisson noise
Résultat de la recherche dans la liste des publications :
4 Articles de conférence |
1 - Formulation contrainte pour la déconvolution de bruit de Poisson.
M. Carlavan et L. Blanc-Féraud. Dans Proc. GRETSI Symposium on Signal and Image Processing, Bordeaux, France, septembre 2011.
Mots-clés : 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 |
= |
{septembre}, |
| 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. |
|
2 - Regularizing parameter estimation for Poisson noisy image restoration.
M. Carlavan et L. Blanc-Féraud. Dans International ICST Workshop on New Computational Methods for Inverse Problems, Paris, France, mai 2011.
Mots-clés : Parameter estimation, discrepancy principle, Poisson noise.
@INPROCEEDINGS{NCMIP11,
|
| author |
= |
{Carlavan, M. and Blanc-Féraud, L.}, |
| title |
= |
{Regularizing parameter estimation for Poisson noisy image restoration}, |
| year |
= |
{2011}, |
| month |
= |
{mai}, |
| booktitle |
= |
{International ICST Workshop on New Computational Methods for Inverse Problems}, |
| address |
= |
{Paris, France}, |
| url |
= |
{http://hal.inria.fr/inria-00590906/fr/}, |
| keyword |
= |
{Parameter estimation, discrepancy principle, Poisson noise} |
| } |
Abstract :
Deblurring images corrupted by Poisson noise is a challeng- ing process which has devoted much research in many ap- plications such as astronomical or biological imaging. This problem, among others, is an ill-posed problem which can be regularized by adding knowledge on the solution. Several methods have therefore promoted explicit prior on the im- age, coming along with a regularizing parameter to moder- ate the weight of this prior. Unfortunately, in the domain of Poisson deconvolution, only a few number of methods have been proposed to select this regularizing parameter which is most of the time set manually such that it gives the best visual results. In this paper, we focus on the use of l1 -norm prior and present two methods to select the regularizing pa- rameter. We show some comparisons on synthetic data using classical image fidelity measures. |
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3 - Parametric blind deconvolution for confocal laser scanning microscopy.
P. Pankajakshan et B. Zhang et L. Blanc-Féraud et Z. Kam et J.C. Olivo-Marin et J. Zerubia. Dans Proc. 29th International Conference of IEEE EMBS (EMBC-07), pages 6531-6534, août 2007.
Mots-clés : Microscopie confocale, Blind Deconvolution, Poisson noise, Variation totale, Algorithme EM, Estimation bayesienne.
Copyright : ©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
@INPROCEEDINGS{Pankajakshan07a,
|
| author |
= |
{Pankajakshan, P. and Zhang, B. and Blanc-Féraud, L. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, |
| title |
= |
{Parametric blind deconvolution for confocal laser scanning microscopy}, |
| year |
= |
{2007}, |
| month |
= |
{août}, |
| booktitle |
= |
{Proc. 29th International Conference of IEEE EMBS (EMBC-07)}, |
| pages |
= |
{6531-6534}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/4352184/4352185/04353856.pdf?tp=&isnumber=&arnumber=4353856}, |
| keyword |
= |
{Microscopie confocale, Blind Deconvolution, Poisson noise, Variation totale, Algorithme EM, Estimation bayesienne} |
| } |
Abstract :
In this paper, we propose a method for the
iterative restoration of fluorescence Confocal Laser Scanning
Microscopic (CLSM) images and parametric estimation of the
acquisition system’s Point Spread Function (PSF). The CLSM is
an optical fluorescence microscope that scans a specimen in 3D
and uses a pinhole to reject most of the out-of-focus light. However,
the quality of the images suffers from two basic physical
limitations. The diffraction-limited nature of the optical system,
and the reduced amount of light detected by the photomultiplier
cause blur and photon counting noise respectively. These images
can hence benefit from post-processing restoration methods
based on deconvolution. An efficient method for parametric
blind image deconvolution involves the simultaneous estimation
of the specimen 3D distribution of fluorescent sources and
the microscope PSF. By using a model for the microscope
image acquisition physical process, we reduce the number of
free parameters describing the PSF and introduce constraints.
The parameters of the PSF may vary during the course of
experimentation, and so they have to be estimated directly from
the observed data. A priori model of the specimen is further
applied to stabilize the alternate minimization algorithm and to
converge to the solutions. |
|
4 - Wavelet-based restoration methods: application to 3D confocal microscopy images.
C. Chaux et L. Blanc-Féraud et J. Zerubia. Dans Proc. SPIE Conference on Wavelets, 2007.
Mots-clés : Restauration, Deconvolution, 3D images, Microscopie confocale, Poisson noise, Ondelettes.
Copyright : Copyright 2007 Society of Photo-Optical Instrumentation Engineers.
This paper was published in Proc. SPIE Conference on Wavelets and is made available as an electronic reprint (preprint) with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
@INPROCEEDINGS{chaux2007,
|
| author |
= |
{Chaux, C. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Wavelet-based restoration methods: application to 3D confocal microscopy images}, |
| year |
= |
{2007}, |
| booktitle |
= |
{Proc. SPIE Conference on Wavelets}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_chaux2007.pdf}, |
| keyword |
= |
{Restauration, Deconvolution, 3D images, Microscopie confocale, Poisson noise, Ondelettes} |
| } |
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