Charles DeledalleChargé de recherche CNRS dans l'Equipe Image Optimisation et Probabilités à l'IMB (Université Bordeaux, France)
charles-alban (dot) deledalle (at) math.u-bordeaux (dot) fr
Visiting professor at University California San Diego in the department of Electronical and Computer Engineering
cdeledalle (at) ucsd (dot) edu
J'ai obtenu le diplome d'Ingénieur de l'EPITA et le diplome du master Sciences et Technologies de l'Univ. Paris VI, tous deux en France, en 2008. En 2011, j'ai soutenu ma thèse du LTCI, Telecom ParisTech, France, en traitement du signal et de l'image et supervisée par Florence Tupin et Loïc Denis. J'ai fait un post-doctorat en mathématiques appliquées au CEREMADE, Univ. Paris IX, France, en 2011-2012, sous la supervision de Gabriel Peyré et Jalal Fadili. Je suis actuellement chercheur CNRS à l'IMB, Univ. Bordeaux, France. Mes recherches incluent le débruitage d'images et les problèmes inverses et en particuliers l'estimation de paramètres. J'ai reçu le prix du meilleur papier étudiant IEEE ICIP en 2010, le prix de thèse ISIS/EEA/GRETSI en 2012 et le prix du journal IEEE TGRS en 2016.
[Voir mon curriculum vitae]
Nouveautés et calendrier
- Dec 13, 2016. I will be a visiting scholar at University California San Diego in the department of Electronical and Computer Engineering from January 1, 2017.
- July 7, 2016. Our former PhD student, Camille Sutour, received both the Aerospace Valley 2016 PhD Award and the University of Bordeaux 2016 "Sciences and Technologies" PhD Award for her thesis in image processing entitled "Numerical night vision system: Automatic restoration and multimodal registration of low light level images".
- April 29, 2016. Our article "NL-SAR: A Unified Nonlocal Framework for Resolution-preserving (Pol)(In)SAR Denoising" (see HAL version) was selected as the winner of the IEEE Geoscience and Remote Sensing Society 2016 Transactions Prize Paper Award!
- April 2, 2016. After four years of research on this topic our paper "The Degrees of Freedom of Partly Smooth Regularizers" has been accepted for publication at the Annals of the Institute of Statistical Mathematics.
- May 18, 2014. Igor Carron has written a post on his blog about our recent paper "Stein Unbiased GrAdient estimator of the Risk (SUGAR) for multiple parameter selection".
- August 28, 2013. Our paper "Non-Local Methods with Shape-Adaptive Patches (NLM-SAP)" has been featured in the most cited articles published since 2011 in International J Mathematical Imaging and Vision.
- Dec 31, 2012. Our paper "Local Behavior of Sparse Analysis Regularization: Applications to Risk Estimation", accepted for publication in Applied and Computational Harmonic Analysis, is now available on-line (pdf, Science Direct (Elsevier)).
- Dec 28, 2012. MooseTeX Beta 1.05 has been released.
- July 6, 2012. I will join l'Institut de mathématiques de Bordeaux (IMB) next autumn.
- June 14, 2012. I have been selected in the competition for a permanent research position at CNRS. I will join a laboratory in mathematics to pursue my research on imaging problems.
- June 14, 2012. Igor Carron has written a post on his blog about our paper "Poisson noise reduction with non-local PCA".
- June 8, 2012. I received the PhD award in Signal, Image and Vision at the 52nd meeting of the EEA Club in Lille (France). This award is jointly delivered by Club EEA, GdR ISIS and GRETSI.
- May 2012. Our recent paper "How to compare noisy patches? Patch similarity beyond Gaussian noise" is featured in the most downloaded articles of the International Journal of Computer Vision with about 900 downloads this last 3 months.
- Monthly. Groupe de Travail Image
- May 31st - June 4th, 2015. Fifth International Conference on Scale Space and Variational Methods in Computer Vision
- April 7-8th, 2014. Second Workshop on Mathematical Analysis of Images in Bordeaux
- Nov 12-14,2012. Workshop on mathematical image processing in Bordeaux
Travaux de recherche
- Principaux intérêts
- Débruitage d'images et problèmes inverses,
- Estimation de risque en restauration d'images,
- Imagerie SAR multi-modalités,
- Descriptions et résultats de mes dernières recherches (fournis avec le logiciel correspondant)
- Generalized SURE for optimal shrinkage of singular values in low-rank matrix denoising,
- MUlti-channel LOgarithm with Gaussian denoising (MuLoG),
- Adaptive Regularization of the NL-means (R-NL),
- Stein Unbiased GrAdient estimator of the Risk (SUGAR),
- Non-Local framework for (Pol)(In)SAR denoising (NL-SAR),
- Poisson Non-Local Sparse PCA,
- Gaussian Patch-PCA (GP-PCA),
- Non-local Methods with Shape-Adaptive Patches (NLM-SAP),
- Non-local Interferogram Estimator (NL-InSAR),
- Poisson NL means,
- Probablistic Patch-Based filter (PPB).
Some of the publications below have appeared in an IEEE journal, Springer journal, Elsevier journal or conference record. By allowing you to download them, I am required to post the following copyright reminder: "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."
Electronic Journal of Statistics, vol. 11, no. 2, pp. 3141-3164, 2017 (Project Euclid, HAL, ArXiv)
We address the question of estimating Kullback-Leibler losses rather than squared losses in recovery problems where the noise is distributed within the exponential family. We exhibit conditions under which these losses can be unbiasedly estimated or estimated with a controlled bias. Simulations on parameter selection problems in image denoising applications with Gamma and Poisson noises illustrate the interest of Kullback-Leibler losses and the proposed estimators.
Charles-Alban Deledalle, Loïc Denis, Sonia Tabti, Florence Tupin
IEEE Transactions on Image Processing, vol. 26, no. 9, pp. 4389-4403, 2017 (IEEE Xplore, recommended pdf, HAL)
Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) imaging. Since most current and planned SAR imaging satellites operate in polarimetric, interferometric or tomographic modes, SAR images are multi-channel and speckle reduction techniques must jointly process all channels to recover polarimetric and interferometric information. The distinctive nature of SAR signal (complex-valued, corrupted by multiplicative fluctuations) calls for the development of specialized methods for speckle reduction. Image denoising is a very active topic in image processing with a wide variety of approaches and many denoising algorithms available, almost always designed for additive Gaussian noise suppression. This paper proposes a general scheme, called MuLoG (MUlti-channel LOgarithm with Gaussian denoising), to include such Gaussian denoisers within a multi-channel SAR speckle reduction technique. A new family of speckle reduction algorithms can thus be obtained, benefiting from the ongoing progress in Gaussian denoising, and offering several speckle reduction results often displaying method-specific artifacts that can be dismissed by comparison between results.
C-A. Deledalle, N. Papadakis, J. Salmon and S. Vaiter
SIAM Journal on Imaging Sciences, vol. 10, no. 1, pp. 243-284, 2017 (epubs SIAM, HAL, ArXiv)
In this paper, we propose a new framework to remove parts of the systematic errors affecting popular restoration algorithms, with a special focus for image processing tasks. Extending ideas that emerged for l1 regularization, we develop an approach that can help re-fitting the results of standard methods towards the input data. Total variation regularizations and non-local means are special cases of interest. We identify important covariant information that should be preserved by the re-fitting method, and emphasize the importance of preserving the Jacobian (w.r.t to the observed signal) of the original estimator. Then, we provide an approach that has a ``twicing'' flavor and allows re-fitting the restored signal by adding back a local affine transformation of the residual term. We illustrate the benefits of our method on numerical simulations for image restoration tasks.
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