Publications of B. Zhang

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• 2 Articles
• 3 Conference articles
• 1 Technical and Research Report

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2 Articles

1 - 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.

2 - Gaussian approximations of fluorescence microscope point-spread function models. B. Zhang and J. Zerubia and J.C. Olivo-Marin. Applied Optics, 46(10): pages 1819-1829, April 2007. Copyright : © 2007 Optical Society of America @ARTICLE{jz_applied_photo, author = {Zhang, B. and Zerubia, J. and Olivo-Marin, J.C.}, title = {Gaussian approximations of fluorescence microscope point-spread function models}, year = {2007}, month = {April}, journal = {Applied Optics}, volume = {46}, number = {10}, pages = {1819-1829}, keyword = {} } Abstract : We comprehensively study the least-squares Gaussian approximations of the diffraction-limited 2D-3D paraxial-nonparaxial point-spread functions (PSFs) of the wide field fluorescence microscope (WFFM), the laser scanning confocal microscope (LSCM), and the disk scanning confocal microscope (DSCM). The PSFs are expressed using the Debye integral. Under an L∞ constraint imposing peak matching, optimal and near-optimal Gaussian parameters are derived for the PSFs. With an L1 constraint imposing energy conservation, an optimal Gaussian parameter is derived for the 2D paraxial WFFM PSF. We found that (1) the 2D approximations are all very accurate; (2) no accurate Gaussian approximation exists for 3D WFFM PSFs; and (3) with typical pinhole sizes, the 3D approximations are accurate for the DSCM and nearly perfect for the LSCM. All the Gaussian parameters derived in this study are in explicit analytical form, allowing their direct use in practical applications.

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3 Conference articles

1 - 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.

2 - 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.

3 - A study of Gaussian approximations of fluorescence microscopy PSF models. B. Zhang and J. Zerubia and J.C. Olivo-Marin. In Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XIII of Proc. SPIE, in press, Vol. 6090, San Jose, USA, January 2006. Copyright : SPIE @INPROCEEDINGS{zerubia_spie06, author = {Zhang, B. and Zerubia, J. and Olivo-Marin, J.C.}, title = {A study of Gaussian approximations of fluorescence microscopy PSF models}, year = {2006}, month = {January}, booktitle = {Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XIII of Proc. SPIE, in press}, volume = {6090}, address = {San Jose, USA}, keyword = {} }

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Technical and Research Report

1 - Parametric blind deconvolution for confocal laser scanning microscopy-proof of concept. P. Pankajakshan and L. Blanc-Féraud and B. Zhang and Z. Kam and J.C. Olivo-Marin and J. Zerubia. Research Report 6493, INRIA, April 2008. Keywords : Confocal Laser Scanning Microscopy, Bayesian restoration, Blind Deconvolution, point spread function, Richardson-Lucy algorithm, Total variation. Copyright : ARIANA/INRIA @TECHREPORT{ppankajakshan08b, author = {Pankajakshan, P. and Blanc-Féraud, L. and Zhang, B. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, title = {Parametric blind deconvolution for confocal laser scanning microscopy-proof of concept}, year = {2008}, month = {April}, institution = {INRIA}, type = {Research Report}, number = {6493}, url = {https://hal.inria.fr/inria-00269265}, pdf = {http://hal.inria.fr/docs/00/27/02/92/PDF/report.pdf}, keyword = {Confocal Laser Scanning Microscopy, Bayesian restoration, Blind Deconvolution, point spread function, Richardson-Lucy algorithm, Total variation} } Résumé : Nous proposons une méthode de restauration itérative d’images de fluorescence CLSM et d’estimation paramétrique de la fonction de flou (PSF) du système d’acquisition. Le CLSM est un microscope qui balaye un échantillon en 3D et utilise une sténopée pour rejeter la lumière en dehors du point de focalisation. Néanmoins, la qualité des images souffre de deux limitations physiques. La première est due à la diffraction due au système optique et la seconde est due à la quantité réduite de lumière détectée par le tube photo-multiplicateur (PMT). Ces limitations induisent respectivement un flou et du bruit de comptage de photons. Les images peuvent alors bénéficier d’un post-traitement de restauration fondé sur la déconvolution. Le problème à traiter est l’estimation simultanée de la distribution 3D de l’échantillon des sources fluorescentes et de la PSF du microscope (i.e. de déconvolution aveugle). En utilisant un modèle de processus physique d’acquisition d’images microscopiques (CLSM), on réduit le nombre de paramètres libres décrivant la PSF et on introduit des contraintes. On introduit aussi des connaissances a priori sur l’échantillon ce qui permet de stabiliser le processus d’estimation et de favoriser la convergence. Des expériences sur des données synthétiques montrent que la PSF peut être estimée avec précision. Des expériences sur des données réelles montrent de bons resultats de déconvolution en comparaison avec le modèle théorique de la PSF du microscope. Abstract : We propose a method for the iterative restoration of fluorescence Confocal Laser Scanning Microscope (CLSM) images with 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 image suffers from two primary physical limitations. The first is due to the diffraction-limited nature of the optical system and the second is due to the reduced amount of light detected by the photomultiplier tube (PMT). These limitations cause blur and photon counting noise respectively. The 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 observation data. We also introduce a priori knowledge of the specimen that permits stabilization of the estimation process and favorizes the convergence. Experiments on simulated data show that the PSF could be estimatedwith a higher degree of accuracy and those done on real data show very good deconvolution results in comparison to the theoretical microscope PSF model.

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