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Publications of Laure Blanc-Féraud
Result of the query in the list of publications :
20 Articles |
1 - On the Illumination Invariance of the Level Lines under Directed Light: Application to Change Detection.
P. Weiss and A. Fournier and L. Blanc-Féraud and G. Aubert. SIAM Journal on Imaging Sciences, 4(1): pages 448-471, March 2011.
Keywords : Level Lines, topographic map, illumination invariance, Change detection, contrast equalization, remote sensing.
@ARTICLE{SIIMS_2011,
|
| author |
= |
{Weiss, P. and Fournier, A. and Blanc-Féraud, L. and Aubert, G.}, |
| title |
= |
{On the Illumination Invariance of the Level Lines under Directed Light: Application to Change Detection}, |
| year |
= |
{2011}, |
| month |
= |
{March}, |
| journal |
= |
{SIAM Journal on Imaging Sciences}, |
| volume |
= |
{4}, |
| number |
= |
{1}, |
| pages |
= |
{448-471}, |
| url |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/SIIMS_2011_Weiss.pdf}, |
| pdf |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/SIIMS_2011_Weiss.pdf}, |
| keyword |
= |
{Level Lines, topographic map, illumination invariance, Change detection, contrast equalization, remote sensing} |
| } |
Abstract :
We analyze the illumination invariance of the level lines of an image. We show that if the scene
surface has Lambertian reflectance and the light is directed, then a necessary and sufficient condition
for the level lines to be illumination invariant is that the three-dimensional scene be developable and
that its albedo satisfy some geometrical constraints. We then show that the level lines are “almost”
invariant for piecewise developable surfaces. Such surfaces fit most of the urban structures. This
allows us to devise a fast and simple algorithm that detects changes between pairs of remotely
sensed images of urban areas, independently of the lighting conditions. We show the effectiveness of
the algorithm both on synthetic OpenGL scenes and real QuickBird images. The synthetic results
illustrate the theory developed in this paper. The two real QuickBird images show that the proposed
change detection algorithm is discriminant. For easy scenes it achieves a rate of 85% detected changes
for 10% false positives, while it reaches a rate of 75% detected changes for 25% false positives on
demanding scenes.
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|
2 - A formal Gamma-convergence approach for the detection of points in 2-D biological images.
D. Graziani and G. Aubert and L. Blanc-Féraud. SIAM Journal on Imaging Sciences, 3(3): pages 578-594, September 2010.
Keywords : points detection, curvature-depending functionals, divergence-measure fields.
@ARTICLE{2,
|
| author |
= |
{Graziani, D. and Aubert, G. and Blanc-Féraud, L.}, |
| title |
= |
{A formal Gamma-convergence approach for the detection of points in 2-D biological images}, |
| year |
= |
{2010}, |
| month |
= |
{September}, |
| journal |
= |
{SIAM Journal on Imaging Sciences}, |
| volume |
= |
{3}, |
| number |
= |
{3}, |
| pages |
= |
{578-594}, |
| url |
= |
{http://hal.inria.fr/inria-00503152/}, |
| keyword |
= |
{points detection, curvature-depending functionals, divergence-measure fields} |
| } |
Abstract :
We propose a new variational model to locate points in 2-dimensional biological images. To this purpose we introduce a suitable functional whose minimizers are given by the points we want to detect. In order to provide numerical experiments we replace this energy with a sequence of a more treatable functionals by means of the notion of Gamma-convergence. |
|
3 - Régularité et parcimonie pour les problèmes inverses en imagerie : algorithmes et comparaisons.
M. Carlavan and P. Weiss and L. Blanc-Féraud. Traitement du Signal, 27(2): pages 189-219, September 2010.
Keywords : Inverse Problems, Regularization, Total variation, Wavelets.
@ARTICLE{TSCarlavan2010,
|
| author |
= |
{Carlavan, M. and Weiss, P. and Blanc-Féraud, L.}, |
| title |
= |
{Régularité et parcimonie pour les problèmes inverses en imagerie : algorithmes et comparaisons}, |
| year |
= |
{2010}, |
| month |
= |
{September}, |
| journal |
= |
{Traitement du Signal}, |
| volume |
= |
{27}, |
| number |
= |
{2}, |
| pages |
= |
{189-219}, |
| url |
= |
{http://hal.inria.fr/inria-00503050/fr/}, |
| pdf |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/TS_Carlavan_Weiss_BlancFeraud_2010.pdf}, |
| keyword |
= |
{Inverse Problems, Regularization, Total variation, Wavelets} |
| } |
Résumé :
Dans cet article, nous nous intéressons à la régularisation de problèmes inverses reposant sur des critères l1 . Nous séparons ces critères en deux catégories : ceux qui favorisent la régularisation des signaux (à variation totale bornée par exemple) et ceux qui expriment le fait qu'un signal admet une représentation parcimonieuse dans un dictionnaire. Dans une première partie, nous donnons quelques éléments de comparaisons théoriques et pratiques sur les deux a priori, pour aider le lecteur à choisir l'un ou l'autre en fonction de son problème. Pour cette étude, nous utilisons les transformées communément utilisées telles que la variation totale, les ondelettes redondantes ou les curvelets. Dans une deuxième partie, nous proposons un état des lieux des algorithmes de premier ordre adaptés à la minimisation de ces critères. |
|
4 - Blind deconvoltion for thin layered confocal imaging.
P. Pankajakshan and B. Zhang and L. Blanc-Féraud and Z. Kam and J.C. Olivo-Marin and J. Zerubia. Applied Optics, 48(22): pages 4437-4448, August 2009.
Keywords : Blind Deconvolution, Confocal microscopy, Inverse Problems.
Copyright : Optical Society of America
@ARTICLE{ppankajakshan09b,
|
| author |
= |
{Pankajakshan, P. and Zhang, B. and Blanc-Féraud, L. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, |
| title |
= |
{Blind deconvoltion for thin layered confocal imaging}, |
| year |
= |
{2009}, |
| month |
= |
{August}, |
| journal |
= |
{Applied Optics}, |
| volume |
= |
{48}, |
| number |
= |
{22}, |
| pages |
= |
{4437-4448}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/39/55/23/PDF/AppliedOpticsPaperTypesetting.pdf}, |
| keyword |
= |
{Blind Deconvolution, Confocal microscopy, Inverse Problems} |
| } |
Abstract :
We propose an alternate minimization algorithm for estimating the point-spread function (PSF) of a confocal laser scanning microscope and the specimen fluorescence distribution. A three-dimensional separable Gaussian model is used to restrict the PSF solution space and a constraint on the specimen is used so as to favor the stabilization and convergence of the algorithm. The results obtained from the simulation show that the PSF can be estimated to a high degree of accuracy, and those on real data show better deconvolution as compared to a full theoretical PSF model. |
|
5 - Efficient schemes for total variation minimization under constraints in image processing.
P. Weiss and L. Blanc-Féraud and G. Aubert. SIAM journal on Scientific Computing, 31(3): pages 2047-2080, 2009.
Keywords : Total variation, l1 norm, nesterov scheme, Rudin Osher Fatemi, fast optimization, real time.
Copyright : Copyright Siam Society for Industrial and Applied
@ARTICLE{SIAM_JSC_PWEISS,
|
| author |
= |
{Weiss, P. and Blanc-Féraud, L. and Aubert, G.}, |
| title |
= |
{Efficient schemes for total variation minimization under constraints in image processing}, |
| year |
= |
{2009}, |
| journal |
= |
{SIAM journal on Scientific Computing}, |
| volume |
= |
{31}, |
| number |
= |
{3}, |
| pages |
= |
{2047-2080}, |
| url |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/SIAM_JSC09_PWEISS.pdf}, |
| pdf |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/SIAM_JSC09_PWEISS.pdf}, |
| keyword |
= |
{Total variation, l1 norm, nesterov scheme, Rudin Osher Fatemi, fast optimization, real time} |
| } |
|
6 - Richardson-Lucy Algorithm with Total Variation Regularization for 3D Confocal Microscope Deconvolution.
N. Dey and L. Blanc-Féraud and C. Zimmer and Z. Kam and P. Roux and J.C. Olivo-Marin and J. Zerubia. Microscopy Research Technique, 69: pages 260-266, April 2006.
Keywords : Confocal microscopy, Variational methods, Total variation, 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 |
= |
{April}, |
| journal |
= |
{Microscopy Research Technique}, |
| volume |
= |
{69}, |
| pages |
= |
{260-266}, |
| url |
= |
{http://dx.doi.org/10.1002/jemt.20294}, |
| keyword |
= |
{Confocal microscopy, Variational methods, Total variation, 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. |
|
7 - An approximation of the Mumford-Shah energy by a family of dicrete edge-preserving functionals.
G. Aubert and L. Blanc-Féraud and R. March. Nonlinear Analysis, 64: pages 1908-1930, 2006.
Keywords : Gamma Convergence, Finite Element, Segmentation.
@ARTICLE{laure-na05,
|
| author |
= |
{Aubert, G. and Blanc-Féraud, L. and March, R.}, |
| title |
= |
{An approximation of the Mumford-Shah energy by a family of dicrete edge-preserving functionals}, |
| year |
= |
{2006}, |
| journal |
= |
{Nonlinear Analysis}, |
| volume |
= |
{64}, |
| pages |
= |
{1908-1930}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_laure-na05.pdf}, |
| keyword |
= |
{Gamma Convergence, Finite Element, Segmentation} |
| } |
Abstract :
We show the Gamma-convergence of a family of discrete functionals to the Mumford and Shah image segmentation functional.
The functionals of the family are constructed by modifying the elliptic approximating functionals proposed by Ambrosio and Tortorelli. The quadratic term of the energy related to the edges of the segmentation is replaced by a nonconvex functional. |
|
8 - Detecting codimension-two objects in an image with Ginzburg-Landau models.
G. Aubert and J.F. Aujol and L. Blanc-Féraud. International Journal of Computer Vision, 65(1-2): pages 29-42, November 2005.
Keywords : Ginzburg-Landau model, Point Detection, Segmentation, PDE, Biological images, SAR Images.
@ARTICLE{laure-ijcv05,
|
| author |
= |
{Aubert, G. and Aujol, J.F. and Blanc-Féraud, L.}, |
| title |
= |
{Detecting codimension-two objects in an image with Ginzburg-Landau models}, |
| year |
= |
{2005}, |
| month |
= |
{November}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{65}, |
| number |
= |
{1-2}, |
| pages |
= |
{29-42}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/GL_IJCV_5.pdf}, |
| keyword |
= |
{Ginzburg-Landau model, Point Detection, Segmentation, PDE, Biological images, SAR Images} |
| } |
Abstract :
In this paper, we propose a new mathematical model for detecting in an image singularities of codimension greater than or equal to two. This means we want to detect points in a 2-D image or points and curves in a 3-D image. We drew one's inspiration from
Ginzburg-Landau (G-L) models which have proved their efficiency for modeling many phenomena in physics. We introduce the model, state its
mathematical properties and give some experimental results demonstrating its capability in image processing. |
|
9 - Image Decomposition into a Bounded Variation Component and an Oscillating Component.
J.F. Aujol and G. Aubert and L. Blanc-Féraud and A. Chambolle. Journal of Mathematical Imaging and Vision, 22(1): pages 71--88, January 2005.
@ARTICLE{BLA05,
|
| author |
= |
{Aujol, J.F. and Aubert, G. and Blanc-Féraud, L. and Chambolle, A.}, |
| title |
= |
{Image Decomposition into a Bounded Variation Component and an Oscillating Component}, |
| year |
= |
{2005}, |
| month |
= |
{January}, |
| journal |
= |
{Journal of Mathematical Imaging and Vision}, |
| volume |
= |
{22}, |
| number |
= |
{1}, |
| pages |
= |
{71--88}, |
| pdf |
= |
{http://springerlink.metapress.com/media/6n99d5dtvj6juld5tw5w/contributions/h/2/0/3/h20366rj1r34567m.pdf}, |
| keyword |
= |
{} |
| } |
|
10 - An adaptive Gaussian model for satellite image deblurring.
A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. IEEE Trans. Image Processing, 13(4), 2004.
@ARTICLE{JAL04a,
|
| author |
= |
{Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{An adaptive Gaussian model for satellite image deblurring}, |
| year |
= |
{2004}, |
| journal |
= |
{IEEE Trans. Image Processing}, |
| volume |
= |
{13}, |
| number |
= |
{4}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/83/28667/01284396.pdf?tp=&arnumber=1284396&isnumber=28667}, |
| keyword |
= |
{} |
| } |
|
11 - Gamma-convergence of discrete functionals with nonconvex perturbation for image classification.
G. Aubert and L. Blanc-Féraud and R. March. SIAM Journal on Numerical Analysis, 12(3): pages 1128--1145, 2004.
@ARTICLE{BLA04,
|
| author |
= |
{Aubert, G. and Blanc-Féraud, L. and March, R.}, |
| title |
= |
{Gamma-convergence of discrete functionals with nonconvex perturbation for image classification}, |
| year |
= |
{2004}, |
| journal |
= |
{SIAM Journal on Numerical Analysis}, |
| volume |
= |
{12}, |
| number |
= |
{3}, |
| pages |
= |
{1128--1145}, |
| keyword |
= |
{} |
| } |
|
12 - Wavelet-based Level Set Evolution for Classification of Textured Images.
J.F. Aujol and G. Aubert and L. Blanc-Féraud. IEEE Trans. Image Processing, 12(12), 2003.
@ARTICLE{aujolGL,
|
| author |
= |
{Aujol, J.F. and Aubert, G. and Blanc-Féraud, L.}, |
| title |
= |
{Wavelet-based Level Set Evolution for Classification of Textured Images}, |
| year |
= |
{2003}, |
| journal |
= |
{IEEE Trans. Image Processing}, |
| volume |
= |
{12}, |
| number |
= |
{12}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/83/28122/01257399.pdf?tp=&arnumber=1257399&isnumber=28122}, |
| keyword |
= |
{} |
| } |
|
13 - Satellite image debluring using complex wavelet packets.
A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. International Journal of Computer Vision, 51(3): pages 205--217, 2003.
@ARTICLE{JalobeaLBFJZ,
|
| author |
= |
{Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Satellite image debluring using complex wavelet packets}, |
| year |
= |
{2003}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{51}, |
| number |
= |
{3}, |
| pages |
= |
{205--217}, |
| pdf |
= |
{http://www.springerlink.com/media/788y661nyh6vwm80hqrl/contributions/t/2/6/0/t26074p520211l84.pdf}, |
| keyword |
= |
{} |
| } |
|
14 - Hyperparameter estimation for satellite image restoration using a MCMC Maximum Likelihood method.
A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. Pattern Recognition, 35(2): pages 341--352, 2002.
@ARTICLE{jalo02h,
|
| author |
= |
{Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Hyperparameter estimation for satellite image restoration using a MCMC Maximum Likelihood method}, |
| year |
= |
{2002}, |
| journal |
= |
{Pattern Recognition}, |
| volume |
= |
{35}, |
| number |
= |
{2}, |
| pages |
= |
{341--352}, |
| keyword |
= |
{} |
| } |
|
15 - A variational model for image classification and restoration.
C. Samson and L. Blanc-Féraud and G. Aubert and J. Zerubia. IEEE Trans. Pattern Analysis ans Machine Intelligence, 22(5): pages 460-472, May 2000.
@ARTICLE{cs00,
|
| author |
= |
{Samson, C. and Blanc-Féraud, L. and Aubert, G. and Zerubia, J.}, |
| title |
= |
{A variational model for image classification and restoration}, |
| year |
= |
{2000}, |
| month |
= |
{May}, |
| journal |
= |
{IEEE Trans. Pattern Analysis ans Machine Intelligence}, |
| volume |
= |
{22}, |
| number |
= |
{5}, |
| pages |
= |
{460-472}, |
| keyword |
= |
{} |
| } |
|
16 - A Level Set Model for Image Classification.
C. Samson and L. Blanc-Féraud and G. Aubert and J. Zerubia. International Journal of Computer Vision, 40(3): pages 187-198, 2000.
@ARTICLE{cs00b,
|
| author |
= |
{Samson, C. and Blanc-Féraud, L. and Aubert, G. and Zerubia, J.}, |
| title |
= |
{A Level Set Model for Image Classification}, |
| year |
= |
{2000}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{40}, |
| number |
= |
{3}, |
| pages |
= |
{187-198}, |
| keyword |
= |
{} |
| } |
|
17 - Some remarks on the equivalence between 2D and 3D classical snakes and geodesic active contours.
L. Blanc-Féraud and G. Aubert. International Journal of Computer Vision, 34(1): pages 19-28, September 1999.
@ARTICLE{lbf99a,
|
| author |
= |
{Blanc-Féraud, L. and Aubert, G.}, |
| title |
= |
{Some remarks on the equivalence between 2D and 3D classical snakes and geodesic active contours}, |
| year |
= |
{1999}, |
| month |
= |
{September}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{34}, |
| number |
= |
{1}, |
| pages |
= |
{19-28}, |
| keyword |
= |
{} |
| } |
|
18 - Non linear regularization for helioseismic inversions. Application for the study of the solar tachocline.
T. Corbard and L. Blanc-Féraud and G. Berthomieu and J. Provost. Astronomy and Astrophysics, (344): pages 696-708, 1999.
@ARTICLE{lbf99b,
|
| author |
= |
{Corbard, T. and Blanc-Féraud, L. and Berthomieu, G. and Provost, J.}, |
| title |
= |
{Non linear regularization for helioseismic inversions. Application for the study of the solar tachocline}, |
| year |
= |
{1999}, |
| journal |
= |
{Astronomy and Astrophysics}, |
| number |
= |
{344}, |
| pages |
= |
{696-708}, |
| keyword |
= |
{} |
| } |
|
19 - Variational approach for edge preserving regularization using coupled PDE's.
S. Teboul and L. Blanc-Féraud and G. Aubert and M. Barlaud. IEEE Trans. Image Processing, 7(3): pages 387-397, March 1998.
@ARTICLE{lbf98,
|
| author |
= |
{Teboul, S. and Blanc-Féraud, L. and Aubert, G. and Barlaud, M.}, |
| title |
= |
{Variational approach for edge preserving regularization using coupled PDE's}, |
| year |
= |
{1998}, |
| month |
= |
{March}, |
| journal |
= |
{IEEE Trans. Image Processing}, |
| volume |
= |
{7}, |
| number |
= |
{3}, |
| pages |
= |
{387-397}, |
| keyword |
= |
{} |
| } |
|
20 - Combined constraints for efficient algebraic regularized methods.
I. Laurette and J. Darcourt and L. Blanc-Féraud and P.M. Koulibaly and M. Barlaud. Physics in Medicine and Biology, 34(4): pages 991-1000, 1998.
@ARTICLE{lbf98a,
|
| author |
= |
{Laurette, I. and Darcourt, J. and Blanc-Féraud, L. and Koulibaly, P.M. and Barlaud, M.}, |
| title |
= |
{Combined constraints for efficient algebraic regularized methods}, |
| year |
= |
{1998}, |
| journal |
= |
{Physics in Medicine and Biology}, |
| volume |
= |
{34}, |
| number |
= |
{4}, |
| pages |
= |
{991-1000}, |
| keyword |
= |
{} |
| } |
|
 top of the page
PhD Thesis and Habilitation |
1 - Sur quelques Problèmes Inverses en Traitement d'Image.
L. Blanc-Féraud. Habilitation à diriger des Recherches, Universite de Nice Sophia Antipolis, July 2000.
Keywords : Partial differential equation, Restoration, Regularization, Gamma Convergence, Variational methods.
@PHDTHESIS{lbf,
|
| author |
= |
{Blanc-Féraud, L.}, |
| title |
= |
{Sur quelques Problèmes Inverses en Traitement d'Image}, |
| year |
= |
{2000}, |
| month |
= |
{July}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| type |
= |
{Habilitation à diriger des Recherches}, |
| url |
= |
{http://www.inria.fr/rrrt/th-020.html}, |
| ps |
= |
{ftp://ftp.inria.fr/INRIA/publication/Theses/TH-020.ps.gz}, |
| keyword |
= |
{Partial differential equation, Restoration, Regularization, Gamma Convergence, Variational methods} |
| } |
Résumé :
Après une présentation générale des problèmes inverses mal posés en imagerie, les méthodes de régularisation linéaires puis non linéaires sont présentées. La préservation des discontinuités (contours d'une image) est abordée conjointement selon 3 approches: stochastique, variationnelle et EDP. Des résultats sont montrés sur plusieurs applications dont la restauration d'image optique satellitaire, la reconstruction SPECT 2D et 3D en imagerie médicale, la diffraction inverse en imagerie microonde. Nous faisons ensuite le lien entre régularisation et segmentation dans l'approche variationnelle initialement introduite par Munford et Shah. Deux modèles ont été proposé pour approcher numériquement les discontinuités dans le cadre de la régularisation : par suite de fonctionnelles "Gamma-convergentes" et par ensemble de niveaux. Après avoir considéré l'exemple de la restauration d'image, nous avons aussi développé ces deux approches pour le problème de la classification d'image satelllitaire. Enfin, le problème de l'estimation des paramètres des fonctionnnelles est abordée et une méthode d'estimation stochastique est proposée dans le cadre de la restauration d'image floue en optique satellitaire. mots cles : methodes variationelles, diffusion (EDP), problemes inverses, regularisation, discontinuites, segmentation d'image, fonctionnelle de Mumford et Shah, Gamma-convergence, ensembles de niveaux, contours actifs, estimation de parametres, methodes MCMC, restauration d'image, classification d'image, reconstruction SPECT, diffraction inverse en imagerie micro-onde. |
Abstract :
We first describe ill-posed inverse problems in image processing, linear and nonlinear regularisation methods. Discontinuity preservation (edges of the image) is jointly presented following three approaches : stochastic, variational and by diffusion process (solving PDE's). Results are shown on several applications such as optical satellite image restoration, 2D and 3D SPECT reconstruction in medical images, inverse diffraction in microwavimages. Then we rely regularisation and segmentation problem in the variational approach as introduced by Mumford and Shah. Tow models have been proposed in order to numerically compute discontinuities in such models : by minimizing sequence of functionals which "Gamma-converge", and by using level sets models. After considering the restoration case, we have developped such methods for the problem of supervised image classification. Finally we have considered the parameter estimation problem for such fonctionnals and we describe a stochastic estimation method for the problem of satellite image restoration. Key-words : variational methods, diffusion (PDE), inverse problems, regularisation, discontinuities, image segmentation, Mumford and Shah functional, Gamma-convergence, level set methods, active contours, parameter estimation, MCMC methods, image restoration, supervised image classification, SPECT reconstruction, inverse diffraction in microwave images. |
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top of the page
58 Conference articles |
1 - Two constrained formulations for deblurring Poisson noisy images.
M. Carlavan and L. Blanc-Féraud. In Proc. IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, September 2011.
Keywords : Poisson deconvolution, discrepancy principle, constrained convex optimization.
@INPROCEEDINGS{ICIP2011_Carlavan,
|
| author |
= |
{Carlavan, M. and Blanc-Féraud, L.}, |
| title |
= |
{Two constrained formulations for deblurring Poisson noisy images}, |
| year |
= |
{2011}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Brussels, Belgium}, |
| url |
= |
{http://hal.inria.fr/inria-00591035/fr/}, |
| keyword |
= |
{Poisson deconvolution, discrepancy principle, constrained convex optimization} |
| } |
Abstract :
Deblurring noisy Poisson images has recently been subject of an increasingly amount of works in many areas such as astronomy or biological imaging. Several methods have promoted explicit prior on the solution to regularize the ill-posed inverse problem and to improve the quality of the image. In each of these methods, a regularizing parameter is introduced to control the weight of the prior. Unfortunately, this regularizing parameter has to be manually set such that it gives the best qualitative results. To tackle this issue, we present in this paper two constrained formulations for the Poisson deconvolution problem, derived from recent advances in regularizing parameter estimation for Poisson noise. We first show how to improve the accuracy of these estimators and how to link these estimators to constrained formulations. We then propose an algorithm to solve the resulting optimization problems and detail how to perform the projections on the constraints. Results on real and synthetic data are presented. |
|
2 - 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. |
|
3 - Restauration d'image dégradée par un flou spatialement variant.
S. Ben Hadj and L. Blanc-Féraud. In Proc. GRETSI Symposium on Signal and Image Processing, Bordeaux, France, September 2011.
@INPROCEEDINGS{SaimaGretsi11,
|
| author |
= |
{Ben Hadj, S. and Blanc-Féraud, L.}, |
| title |
= |
{Restauration d'image dégradée par un flou spatialement variant}, |
| year |
= |
{2011}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
= |
{Bordeaux, France}, |
| url |
= |
{http://hal.inria.fr/inria-00625519/fr/}, |
| keyword |
= |
{} |
| } |
Résumé :
La plupart des techniques de restauration d’images disponibles supposent que le flou est spatialement invariant. Néanmoins, différents
phénomènes physiques liés aux propriétés de l’optique font que les dégradations peuvent être différentes selon les régions de l’image. Dans ce
travail, nous considérons un modèle de PSF invariant par zone avec des transitions régulières entre les zones afin de prendre en compte la
variation du flou dans l’image. Nous développons pour ce modèle, une méthode de déconvolution adaptée, par minimisation d’un critère avec
une régularisation par variation totale. Nous nous appuyions sur une méthode rapide de minimisation par décomposition de domaine qui a été
récemment développée par Fornasier et al., 2009. Nous obtenons ainsi un algorithme où la minimisation du critère est effectuée en parallèle sur
les différentes zones de l’image, tout en prenant en compte les estimées dans les zones voisines des sous-images considérées, de sorte que la
solution finale soit le minimum du critère où le flou est variant spatialement. |
Abstract :
In most of the existing image restoration techniques, the blur is assumed to be spatially invariant. However, different physical
phenomena related to the optic’s properties makes that degradations may change according to the image’s areas. In this work, we consider a
piecewise-varying PSF model with smooth transitions between areas in order to take into account blur variation in the image. We develop for
this model, a convenient deconvolution method by minimizing a criterion with a total variation regularization. We rely on a fast minimization
method using a domain decomposition method that was recently developed by Fornasier et al. 2009. We thus obtain an algorithm where the
criterion minimization is performed in a parallel way on different areas of the image, taking into account the estimates of neighboring areas of
the considered sub-image, so that the final solution is space-varying deconvolved. |
|
4 - Regularizing parameter estimation for Poisson noisy image restoration.
M. Carlavan and L. Blanc-Féraud. In International ICST Workshop on New Computational Methods for Inverse Problems, Paris, France, May 2011.
Keywords : 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 |
= |
{May}, |
| 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. |
|
5 - A new variational method for preserving point-like and curve-like singularities in 2d images.
D. Graziani and L. Blanc-Féraud and G. Aubert. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, May 2011.
Keywords : Convex optimization, nesterov scheme, laplacian operator.
@INPROCEEDINGS{ICASSP_Graziani11,
|
| author |
= |
{Graziani, D. and Blanc-Féraud, L. and Aubert, G.}, |
| title |
= |
{A new variational method for preserving point-like and curve-like singularities in 2d images}, |
| year |
= |
{2011}, |
| month |
= |
{May}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Prague, Czech Republic}, |
| url |
= |
{http://hal.inria.fr/inria-00592603/fr/}, |
| keyword |
= |
{Convex optimization, nesterov scheme, laplacian operator} |
| } |
Abstract :
We propose a new variational method to restore point-like and curve-like singularities in 2-D images. As points and open curves are fine structures, they are difficult to restore by means of first order derivative operators computed in the noisy image. In this paper we propose to use the Laplacian operator of the observed intensity, since it becomes singular at points and curves. Then we propose to restore these singularities by introducing suitable regularization involving the l-1-norm of the Laplacian operator. Results are shown on synthetic an real data.
|
|
6 - Wavefront sensing for aberration modeling in fluorescence MACROscopy.
P. Pankajakshan and A. Dieterlen and G. Engler and Z. Kam and L. Blanc-Féraud and J. Zerubia and J.C. Olivo-Marin. In Proc. IEEE International Symposium on Biomedical Imaging (ISBI), Chicago, USA, April 2011.
Keywords : fluorescence MACROscopy , phase retrieval, field aberration.
@INPROCEEDINGS{PanjakshanISBI2011,
|
| author |
= |
{Pankajakshan, P. and Dieterlen, A. and Engler, G. and Kam, Z. and Blanc-Féraud, L. and Zerubia, J. and Olivo-Marin, J.C.}, |
| title |
= |
{Wavefront sensing for aberration modeling in fluorescence MACROscopy}, |
| year |
= |
{2011}, |
| month |
= |
{April}, |
| booktitle |
= |
{Proc. IEEE International Symposium on Biomedical Imaging (ISBI)}, |
| address |
= |
{Chicago, USA}, |
| url |
= |
{http://hal.inria.fr/inria-00563988/en/}, |
| keyword |
= |
{fluorescence MACROscopy , phase retrieval, field aberration} |
| } |
Abstract :
In this paper, we present an approach to calculate the wavefront in
the back pupil plane of an objective in a fluorescent MACROscope.
We use the three-dimensional image of a fluorescent bead because it
contains potential pupil information in the ‘far’ out-of-focus planes
for sensing the wavefront at the back focal plane of the objective.
Wavefront sensing by phase retrieval technique is needed for several
reasons. Firstly, the point-spread function of the imaging system
can be calculated from the estimated pupil phase and used for image
restoration. Secondly, the aberrations in the optics of the objective
can be determined by studying this phase. Finally, the estimated
wavefront can be used to correct the aberrated optical path with-
out a wavefront sensor. In this paper, we estimate the wavefront of
a MACROscope optical system by using Bayesian inferencing and
derive the Gerchberg-Saxton algorithm as a special case. |
|
7 - Point-spread function model for fluorescence MACROscopy imaging.
P. Pankajakshan and Z. Kam and A. Dieterlen and G. Engler and L. Blanc-Féraud and J. Zerubia and J.C. Olivo-Marin. In Asilomar Conference on Signals, Systems and Computers, pages 1364-136, Pacific Grove, CA, USA , November 2010.
Keywords : fluorescence MACROscopy , point-spread function, pupil function, vignetting .
@INPROCEEDINGS{PanjakshanASILOMAR2010,
|
| author |
= |
{Pankajakshan, P. and Kam, Z. and Dieterlen, A. and Engler, G. and Blanc-Féraud, L. and Zerubia, J. and Olivo-Marin, J.C.}, |
| title |
= |
{Point-spread function model for fluorescence MACROscopy imaging}, |
| year |
= |
{2010}, |
| month |
= |
{November}, |
| booktitle |
= |
{Asilomar Conference on Signals, Systems and Computers}, |
| pages |
= |
{1364-136}, |
| address |
= |
{Pacific Grove, CA, USA }, |
| url |
= |
{http://hal.inria.fr/inria-00555940_v1/}, |
| keyword |
= |
{fluorescence MACROscopy , point-spread function, pupil function, vignetting } |
| } |
Abstract :
In this paper, we model the point-spread function (PSF) of a fluorescence MACROscope with a field aberration. The MACROscope is an imaging arrangement that is designed to directly study small and large specimen preparations without physically sectioning them. However, due to the different optical components of the MACROscope, it cannot achieve the condition of lateral spatial invariance for all magnifications. For example, under low zoom settings, this field aberration becomes prominent, the PSF varies in the lateral field, and is proportional to the distance from the center of the field. On the other hand, for larger zooms, these aberrations become gradually absent. A computational approach to correct this aberration often relies on an accurate knowledge of the PSF. The PSF can be defined either theoretically using a scalar diffraction model or empirically by acquiring a three-dimensional image of a fluorescent bead that approximates a point source. The experimental PSF is difficult to obtain and can change with slight deviations from the physical conditions. In this paper, we model the PSF using the scalar diffraction approach, and the pupil function is modeled by chopping it. By comparing our modeled PSF with an experimentally obtained PSF, we validate our hypothesis that the spatial variance is caused by two limiting optical apertures brought together on different conjugate planes. |
|
8 - Detection and tracking of threats in aerial infrared images by a minimal path approach.
G. Aubert and A. Baudour and L. Blanc-Féraud and L. Guillot and Y. Le Guilloux. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Dallas, Texas, USA, March 2010.
@INPROCEEDINGS{ICASSP10,
|
| author |
= |
{Aubert, G. and Baudour, A. and Blanc-Féraud, L. and Guillot, L. and Le Guilloux, Y.}, |
| title |
= |
{Detection and tracking of threats in aerial infrared images by a minimal path approach}, |
| year |
= |
{2010}, |
| month |
= |
{March}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Dallas, Texas, USA}, |
| keyword |
= |
{} |
| } |
|
9 - Algorithme rapide pour la restauration d'image régularisée sur les coefficients d'ondelettes.
M. Carlavan and P. Weiss and L. Blanc-Féraud and J. Zerubia. In Proc. Symposium on Signal and Image Processing (GRETSI), Dijon, France, September 2009.
Keywords : Deconvolution, nesterov scheme, Wavelets, l1 norm.
@INPROCEEDINGS{GRETSICarlavan09,
|
| author |
= |
{Carlavan, M. and Weiss, P. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Algorithme rapide pour la restauration d'image régularisée sur les coefficients d'ondelettes}, |
| year |
= |
{2009}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. Symposium on Signal and Image Processing (GRETSI)}, |
| address |
= |
{Dijon, France}, |
| url |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/CarlavanGretsi09.pdf}, |
| pdf |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/CarlavanGretsi09.pdf}, |
| keyword |
= |
{Deconvolution, nesterov scheme, Wavelets, l1 norm} |
| } |
Résumé :
De nombreuses méthodes de restauration d'images consistent à minimiser une énergie convexe. Nous nous focalisons sur l'utilisation de ces méthodes et considérons la minimisation de deux critères contenant une norme l1 des coefficients en ondelettes. La plupart des travaux publiés récemment proposent un critère à minimiser dans le domaine des coefficients en ondelettes, utilisant ainsi un a priori de parcimonie. Nous proposons un algorithme rapide et des résultats de déconvolution par minimisation d'un critère dans le domaine image, avec un a priori de régularité exprimé dans le domaine image utilisant une décomposition redondante sur une trame. L'algorithme et le modèle proposés semblent originaux pour ce problème en traitement d'images et sont performants en terme de temps de calculs et de qualité de restauration. Nous montrons des comparaisons entre les deux types d' a priori. |
Abstract :
Many image restoration techniques are based on convex energy minimization. We focus on the use of these techniques and consider the minimization of two criteria holding a l1-norm of wavelet coefficients. Most of the recent research works are based on the minimization of a criterion in the wavelet coefficients domain, namely as a sparse prior. We propose a fast algorithm and deconvolution results obtained by minimizing a criterion in the image domain using a redundant decomposition on a frame. The algorithm and model proposed are unusual for this problem and very efficient in term of computing time and quality of restoration results. We show comparisons between the two different priors. |
|
10 - Estimation d'hyperparamètres pour la résolution de problèmes inverses à l'aide d'ondelettes.
C. Chaux and L. Blanc-Féraud. In Proc. Symposium on Signal and Image Processing (GRETSI), Dijon, France, September 2009.
@INPROCEEDINGS{ChauxGRETSI09,
|
| author |
= |
{Chaux, C. and Blanc-Féraud, L.}, |
| title |
= |
{Estimation d'hyperparamètres pour la résolution de problèmes inverses à l'aide d'ondelettes}, |
| year |
= |
{2009}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. Symposium on Signal and Image Processing (GRETSI)}, |
| address |
= |
{Dijon, France}, |
| url |
= |
{http://hdl.handle.net/2042/28911}, |
| keyword |
= |
{} |
| } |
Résumé :
Nous nous intéressons à l'estimation des paramètres de régularisation pour la restauration d'image floue et bruitée. Dans l'approche variationnelle, la restauration consiste à minimiser un critère convexe composé d'un terme de rappel aux données (quadratique) et d'un terme de régularisation (norme I1) opérant dans le domaine ondelettes. Nous proposons une méthode d'estimation des paramètres de régularisation (hyperparamètres, un par sous-bande) par maximum de vraisemblance, à partir de la seule image observée. La difficulté de l'estimation en données incomplètes est de pouvoir échantillonner des lois sur des champs de variables aléatoires dont les interactions entre voisins sont étendues, du fait de l'opérateur linéaire de flou. Nous proposons une méthode qui permet de calculer ces échantillons par MCMC (échantillonnage de Gibbs et Metropolis-Hastings). Pour l'estimation, nous utilisons une méthode de gradient. Les résultats de simulation obtenus montrent la faisabilité de la méthode et ses bonnes performances en terme d'estimation. |
|
11 - A proximal method for inverse problems in image processing.
P. Weiss and L. Blanc-Féraud. In Proc. European Signal Processing Conference (EUSIPCO), Glasgow, Scotland, August 2009.
Keywords : Extragradient method, proximal method, Image decomposition, Meyer's model, convergence rate.
@INPROCEEDINGS{PWEISS_Eusipco,
|
| author |
= |
{Weiss, P. and Blanc-Féraud, L.}, |
| title |
= |
{A proximal method for inverse problems in image processing}, |
| year |
= |
{2009}, |
| month |
= |
{August}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{Glasgow, Scotland}, |
| url |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/Eusipco09.pdf}, |
| pdf |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/Eusipco09.pdf}, |
| keyword |
= |
{Extragradient method, proximal method, Image decomposition, Meyer's model, convergence rate} |
| } |
Abstract :
In this paper, we present a new algorithm to solve some inverse problems coming from the field of image processing. The models we study consist in minimizing a regularizing, convex criterion under a convex and compact set. The main idea of our scheme consists in solving the underlying variational inequality with a proximal method rather than the initial convex problem. Using recent results of A. Nemirovski [13], we show that the scheme converges at least as O(1/k) (where k is the iteration counter). This is in some sense an optimal rate of convergence. Finally, we compare this approach to some others on a problem of image cartoon+texture decomposition. |
|
12 - Complex wavelet regularization for solving inverse problems in remote sensing.
M. Carlavan and P. Weiss and L. Blanc-Féraud and J. Zerubia. In Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, July 2009.
Keywords : Deconvolution, Dual smoothing, nesterov scheme, remote sensing, wavelet.
|
13 - Point-spread function retrieval for fluorescence microscopy.
P. Pankajakshan and L. Blanc-Féraud and Z. Kam and J. Zerubia. In Proc. IEEE International Symposium on Biomedical Imaging (ISBI), Publ. IEEE, Org. IEEE, Boston, USA, June 2009.
Keywords : fluorescence microscopy, point spread function, EM algorithm, Deconvolution.
Copyright : Copyright 2009 IEEE. Published in the 2009 International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2009), scheduled for June 28 - July 1, 2009 in Boston, Massachusetts, U.S.A. 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. Contact: Manager, Copyrights and Permissions / IEEE Service Center / 445 Hoes Lane / P.O. Box 1331 / Piscataway, NJ 08855-1331, USA. Telephone: + Intl. 908-562-3966.
@INPROCEEDINGS{ppankajakshan09a,
|
| author |
= |
{Pankajakshan, P. and Blanc-Féraud, L. and Kam, Z. and Zerubia, J.}, |
| title |
= |
{Point-spread function retrieval for fluorescence microscopy}, |
| year |
= |
{2009}, |
| month |
= |
{June}, |
| booktitle |
= |
{Proc. IEEE International Symposium on Biomedical Imaging (ISBI)}, |
| publisher |
= |
{IEEE}, |
| organization |
= |
{IEEE}, |
| address |
= |
{Boston, USA}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/39/55/34/PDF/pankajakshan.pdf}, |
| keyword |
= |
{fluorescence microscopy, point spread function, EM algorithm, Deconvolution} |
| } |
Abstract :
In this paper we propose a method for retrieving the Point-Spread Function (PSF) of an imaging system given the observed images of fluorescent microspheres. Theoretically calculated PSFs often lack the experimental or microscope specific signatures while empirically obtained data are either over sized or (and) too noisy. The effect of noise and the influence of the microsphere size can be mitigated from the experimental data by using a Maximum Likelihood Expectation Maximization (MLEM) algorithm. The true experimental parameters can then be estimated by fitting the result to a model based on the scalar diffraction theory. The algorithm was tested on some simulated data and the results obtained validate the usefulness of the approach for retrieving the PSF from measured data. |
|
14 - A new variational method to detect points in biological images.
D. Graziani and L. Blanc-Féraud and G. Aubert. In ISBI'09, Org. IEEE International Symposium on Biomedical Imaging, Boston, USA, June 2009.
Keywords : Biological images, points detection, Gamma-convergence.
@INPROCEEDINGS{GRAZIANI_ISBI2009,
|
| author |
= |
{Graziani, D. and Blanc-Féraud, L. and Aubert, G.}, |
| title |
= |
{A new variational method to detect points in biological images}, |
| year |
= |
{2009}, |
| month |
= |
{June}, |
| booktitle |
= |
{ISBI'09}, |
| organization |
= |
{IEEE International Symposium on Biomedical Imaging}, |
| address |
= |
{Boston, USA}, |
| url |
= |
{http://dx.doi.org/10.1109/ISBI.2009.5193301}, |
| keyword |
= |
{Biological images, points detection, Gamma-convergence} |
| } |
Abstract :
We propose a new variational method to isolate points in biological images. As points are fine structures they are difficult to detect by derivative operators computed in the noisy image. In this paper we propose to compute a vector field from the observed intensity so that its divergence explodes at points. As the image could contains spots but also noise and curves where the divergence also blows up, we propose to capture spots by introducing suitable energy whose minimizers are given by the points we want to detect. In order to provide numerical experiments we approximate this energy by means of a sequence of more treatable functionals by a Gamma-convergence approach. Results are shown on synthetic and biological images. |
|
15 - Smoothing techniques for convex problems. Applications in image processing..
P. Weiss and M. Carlavan and L. Blanc-Féraud and J. Zerubia. In Proc. SAMPTA (international conference on Sampling Theory and Applications), Marseille, France, May 2009.
Keywords : nesterov scheme, convergence rate, Dual smoothing.
@INPROCEEDINGS{PWEISS_SAMPTA09,
|
| author |
= |
{Weiss, P. and Carlavan, M. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Smoothing techniques for convex problems. Applications in image processing.}, |
| year |
= |
{2009}, |
| month |
= |
{May}, |
| booktitle |
= |
{Proc. SAMPTA (international conference on Sampling Theory and Applications)}, |
| address |
= |
{Marseille, France}, |
| url |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/Eusipco09.pdf}, |
| pdf |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/Sampta09.pdf}, |
| keyword |
= |
{nesterov scheme, convergence rate, Dual smoothing} |
| } |
Abstract :
In this paper, we present two algorithms to solve some inverse problems coming from the field of image processing. The problems we study are convex and can be expressed simply as sums of lp-norms of affine transforms of the image. We propose 2 different techniques. They are - to the best of our knowledge - new in the domain of image processing and one of them is new in the domain of mathematical programming. Both methods converge to the set of minimizers. Additionally, we show that they converge at least as O(1/N) (where N is the iteration counter) which is in some sense an ``optimal'' rate of convergence. Finally, we compare these approaches to some others on a toy problem of image super-resolution with impulse noise. |
|
16 - A contrast equalization procedure for change detection algorithms: applications to remotely sensed images of urban areas.
A. Fournier and P. Weiss and L. Blanc-Féraud and G. Aubert. In International Conference on Pattern Recognition (ICPR), Tampa, USA, December 2008.
Keywords : Change detection, Level Lines, remote sensing.
Copyright : ©2008 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{l_lines_icpr08,
|
| author |
= |
{Fournier, A. and Weiss, P. and Blanc-Féraud, L. and Aubert, G.}, |
| title |
= |
{A contrast equalization procedure for change detection algorithms: applications to remotely sensed images of urban areas}, |
| year |
= |
{2008}, |
| month |
= |
{December}, |
| booktitle |
= |
{International Conference on Pattern Recognition (ICPR)}, |
| address |
= |
{Tampa, USA}, |
| url |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/icpr2008.pdf}, |
| pdf |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/icpr2008.pdf}, |
| keyword |
= |
{Change detection, Level Lines, remote sensing} |
| } |
|
17 - Blind deconvolution for diffraction-limited fluorescence microscopy.
P. Pankajakshan and B. Zhang and L. Blanc-Féraud and Z. Kam and J.C. Olivo-Marin and J. Zerubia. In Proc. IEEE International Symposium on Biomedical Imaging (ISBI), pages 740-743, Paris, France, May 2008.
Keywords : Confocal microscopy, Blind Deconvolution, point spread function, Richardson-Lucy algorithm, total variation regularization.
Copyright : This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.
@INPROCEEDINGS{ppankajakshan08a,
|
| author |
= |
{Pankajakshan, P. and Zhang, B. and Blanc-Féraud, L. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, |
| title |
= |
{Blind deconvolution for diffraction-limited fluorescence microscopy}, |
| year |
= |
{2008}, |
| month |
= |
{May}, |
| booktitle |
= |
{Proc. IEEE International Symposium on Biomedical Imaging (ISBI)}, |
| pages |
= |
{740-743}, |
| address |
= |
{Paris, France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2008_ppankajakshan08a.pdf}, |
| keyword |
= |
{Confocal microscopy, Blind Deconvolution, point spread function, Richardson-Lucy algorithm, total variation regularization} |
| } |
Abstract :
Optical Sections of biological samples obtained from a fluorescence Confocal Laser Scanning Microscopes (CLSM) are often degraded by out-of-focus blur and photon counting noise. Such physical constraints on the observation are a result of the diffraction-limited nature of the optical system, and the reduced amount of light detected by the photomultiplier respectively. Hence, the image stacks can benefit from postprocessing restoration methods based on deconvolution. The parameters of the acquisition system’s Point Spread Function (PSF) may vary during the course of experimentation, and so they have to be estimated directly from the observation data. We describe here an alternate minimization algorithm for the simultaneous blind estimation of the specimen 3D distribution of fluorescent sources and the PSF. Experimental results on real data show that the algorithm provides very good deconvolution results in comparison to theoretical microscope PSF models. |
|
18 - Compression artifacts reduction using variational methods: algorithms and experimental study.
P. Weiss and L. Blanc-Féraud and T. Andre and M. Antonini. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Las Vegas, USA, March 2008.
Keywords : compression artifact, fast l1 optimization, Total variation, contrast enhancement, nesterov scheme, jpeg2000.
Copyright :
@INPROCEEDINGS{ICASSP_WEISS,
|
| author |
= |
{Weiss, P. and Blanc-Féraud, L. and Andre, T. and Antonini, M.}, |
| title |
= |
{Compression artifacts reduction using variational methods: algorithms and experimental study}, |
| year |
= |
{2008}, |
| month |
= |
{March}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Las Vegas, USA}, |
| url |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/icassp2008.pdf}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2008_ICASSP_WEISS.pdf}, |
| keyword |
= |
{compression artifact, fast l1 optimization, Total variation, contrast enhancement, nesterov scheme, jpeg2000} |
| } |
|
19 - SATELLITE IMAGE RECONSTRUCTION FROM AN IRREGULAR SAMPLING.
E. Bughin and L. Blanc-Féraud and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Las Vegas, USA, March 2008.
Keywords : Irregular sampling, Variational methods, Fourier analysis, Satellite imaging.
Copyright :
@INPROCEEDINGS{Bughin08,
|
| author |
= |
{Bughin, E. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{SATELLITE IMAGE RECONSTRUCTION FROM AN IRREGULAR SAMPLING}, |
| year |
= |
{2008}, |
| month |
= |
{March}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Las Vegas, USA}, |
| url |
= |
{http://hal.inria.fr/docs/00/27/89/19/PDF/bughinICASSP08.pdf}, |
| keyword |
= |
{Irregular sampling, Variational methods, Fourier analysis, Satellite imaging} |
| } |
|
20 - Sur la complexite et la rapidite d’algorithmes pour la minimisation de la variation totale sous contraintes.
P. Weiss and L. Blanc-Féraud and G. Aubert. In Proc. Symposium on Signal and Image Processing (GRETSI), Troyes, France, September 2007.
Keywords : l1 norm minimization, compression noise denoising, optimal algorithm, convex analysis, Total variation, nesterov scheme.
@INPROCEEDINGS{Pierre Weiss,
|
| author |
= |
{Weiss, P. and Blanc-Féraud, L. and Aubert, G.}, |
| title |
= |
{Sur la complexite et la rapidite d’algorithmes pour la minimisation de la variation totale sous contraintes}, |
| year |
= |
{2007}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. Symposium on Signal and Image Processing (GRETSI)}, |
| address |
= |
{Troyes, France}, |
| url |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/Gretsi_WeissBlancFeraudAubert_2010.PDF}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Pierre Weiss.pdf}, |
| keyword |
= |
{l1 norm minimization, compression noise denoising, optimal algorithm, convex analysis, Total variation, nesterov scheme} |
| } |
|
21 - Parametric blind deconvolution for confocal laser scanning microscopy.
P. Pankajakshan and B. Zhang and L. Blanc-Féraud and Z. Kam and J.C. Olivo-Marin and J. Zerubia. In Proc. 29th International Conference of IEEE EMBS (EMBC-07), pages 6531-6534, August 2007.
Keywords : Confocal microscopy, Blind Deconvolution, Poisson noise, Total variation, EM algorithm, Bayesian estimation.
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 |
= |
{August}, |
| 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 |
= |
{Confocal microscopy, Blind Deconvolution, Poisson noise, Total variation, EM algorithm, Bayesian estimation} |
| } |
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. |
|
22 - Wavelet-based restoration methods: application to 3D confocal microscopy images.
C. Chaux and L. Blanc-Féraud and J. Zerubia. In Proc. SPIE Conference on Wavelets, 2007.
Keywords : Restoration, Deconvolution, 3D images, Confocal microscopy, Poisson noise, Wavelets.
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 |
= |
{Restoration, Deconvolution, 3D images, Confocal microscopy, Poisson noise, Wavelets} |
| } |
|
23 - Detection and Completion of Filaments: A Vector Field and PDE Approach.
A. Baudour and G. Aubert and L. Blanc-Féraud. In SSVM 2007, LNCS 4485 proceedings, 2007.
@INPROCEEDINGS{ssvm2007,
|
| author |
= |
{Baudour, A. and Aubert, G. and Blanc-Féraud, L.}, |
| title |
= |
{Detection and Completion of Filaments: A Vector Field and PDE Approach}, |
| year |
= |
{2007}, |
| booktitle |
= |
{ SSVM 2007, LNCS 4485 proceedings}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_ssvm2007.pdf}, |
| keyword |
= |
{} |
| } |
|
24 - Détection et Complétion de Filaments: une approche variationelle et vectorielle.
A. Baudour and G. Aubert and L. Blanc-Féraud. In Colloque Gretsi Troyes, 2007, 2007.
@INPROCEEDINGS{ Gretsi 2007,
|
| author |
= |
{Baudour, A. and Aubert, G. and Blanc-Féraud, L.}, |
| title |
= |
{Détection et Complétion de Filaments: une approche variationelle et vectorielle}, |
| year |
= |
{2007}, |
| booktitle |
= |
{Colloque Gretsi Troyes, 2007}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_ Gretsi 2007.pdf}, |
| keyword |
= |
{} |
| } |
|
25 - A Restoration Method for Confocal Microscopy Using Complex Wavelet Transform.
G. Pons Bernad and L. Blanc-Féraud and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Philadelphia, Pennsylvania, USA, March 2005.
@INPROCEEDINGS{pons_icassp2005,
|
| author |
= |
{Pons Bernad, G. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{A Restoration Method for Confocal Microscopy Using Complex Wavelet Transform}, |
| year |
= |
{2005}, |
| month |
= |
{March}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Philadelphia, Pennsylvania, USA}, |
| keyword |
= |
{} |
| } |
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 and/or denoising methods have been proposed to reduce these degradations.Here we propose a wavelet denoising method, which turns out to be very effective for three-dimensional confocal images. To obtain a translation and rotation invariant algorithm, we have developped the 3D Complex Wavelet Transform introduced by N. Kingsbury. These wavelets allow moreover a better directional selectivity of the wavelet coefficients. We show on simulated and real biological data the good performances of this algorithm. |
|
26 - Image Disocclusion Using a Probabilistic Gradient Orientation.
E. Villéger and G. Aubert and L. Blanc-Féraud. In Proc. International Conference on Pattern Recognition (ICPR), Cambridge, United Kingdom, August 2004.
@INPROCEEDINGS{Villeger04,
|
| author |
= |
{Villéger, E. and Aubert, G. and Blanc-Féraud, L.}, |
| title |
= |
{Image Disocclusion Using a Probabilistic Gradient Orientation}, |
| year |
= |
{2004}, |
| month |
= |
{August}, |
| booktitle |
= |
{Proc. International Conference on Pattern Recognition (ICPR)}, |
| address |
= |
{Cambridge, United Kingdom}, |
| keyword |
= |
{} |
| } |
|
27 - A $l^1$-unified variational framework for image restoration.
J. Bect and L. Blanc-Féraud and G. Aubert and A. Chambolle. In Proc. European Conference on Computer Vision (ECCV), Vol. LNCS 3024, pages 1--13, Ed. T. Pajdla and J. Matas, Publ. Springer, Prague, Czech Republic, May 2004.
@INPROCEEDINGS{eccv04,
|
| author |
= |
{Bect, J. and Blanc-Féraud, L. and Aubert, G. and Chambolle, A.}, |
| title |
= |
{A $l^1$-unified variational framework for image restoration}, |
| year |
= |
{2004}, |
| month |
= |
{May}, |
| booktitle |
= |
{Proc. European Conference on Computer Vision (ECCV)}, |
| volume |
= |
{LNCS 3024}, |
| pages |
= |
{1--13}, |
| editor |
= |
{T. Pajdla and J. Matas}, |
| publisher |
= |
{Springer}, |
| address |
= |
{Prague, Czech Republic}, |
| keyword |
= |
{} |
| } |
|
28 - Deconvolution in 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. French-Danish Workshop on Spatial Statistics and Image Analysis in Biology (SSIAB), pages 117--120, May 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 |
= |
{May}, |
| booktitle |
= |
{Proc. French-Danish Workshop on Spatial Statistics and Image Analysis in Biology (SSIAB)}, |
| pages |
= |
{117--120}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/ndey/download/2004.05-SSIAB.Dey.pdf}, |
| keyword |
= |
{} |
| } |
|
29 - 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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