Charles Deledalle's Résumé

Charles Deledalle picture


  • Invited researcher on speckle denoising for high-resolution F-SAR images
    German aerospace center - DLR (Oberpfaffenhofen, Germany),
    From April, 2011 to May, 2011
  • Search Engine Opitimisation
    AddedLifeValue (Düsseldorf, Deutschland)
    Study of the Google policy, setting of Divorce.fr in 2nd rank in Google.fr
    From April, 2007 to August, 2007
  • Associated member with M2pi Ltd.
    M2pi Ltd. (Paris, France)
    Software development in C/C++, Java/JSP and Ruby on Rails
    From 2002 to 2006


Telecom ParisTech
  • Master of Science degree in Sciences and Technologies
    Université Pierre et Marie Currie (Paris 6, France)
    Specialize in Artificial Intelligence and Decision Theory
    Major in Information retrieval and Multimedia
    Graduated with first class honours
    From September, 2007 to Septembre, 2008
Utrecht University
  • Exchange student at Utrecht university
    Utrecht University (Utrecht, Netherlands)
    Depatment of Computer Science
    Section of Agent Technologies
    From September, 2006 to January, 2007
  • Baccalauréat scientifique
    Lycée Notre-Dame (France)
    Major in mathematics
    Graduated with honours
    June, 2003


Telecom ParisTech
  • Subject:
    Image denoising beyond additive Gaussian noise
    Patch-based estimators and their application to SAR imagery
  • Defended on November 15, 2011. Jury members:
    Jose Bioucas DiasInstituto de TelecomunicaçõesRapporteur
    Jean-François GiovannelliUniviversité Bordeaux 1Examinateur
    Laure Blanc-FéraudCNRS -- Sophia AntipolisPrésidente
    Jean-Michel MorelENS CachanRapporteur
    Philippe RéfrégierEcole centrale de MarseilleRapporteur
    Andreas ReigberGerman Aerospace Center, DLRExaminateur

Recent publications

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."

Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100x speed-up,
Shibin Parameswaran, Charles-Alban Deledalle, Loïc Denis, Truong Q. Nguyen
IEEE Transactions on Image Processing, vol. 28, no. 2, pp. 687-698, 2019 (IEEE Xplore, recommended pdf, HAL, ArXiv)
Presented at 5G and Beyond forum, May 2018, La Jolla, CA, USA (poster)
Image restoration methods aim to recover the underlying clean image from corrupted observations. The Expected Patch Log-likelihood (EPLL) algorithm is a powerful image restoration method that uses a Gaussian mixture model (GMM) prior on the patches of natural images. Although it is very effective for restoring images, its high runtime complexity makes EPLL ill-suited for most practical applications. In this paper, we propose three approximations to the original EPLL algorithm. The resulting algorithm, which we call the fast-EPLL (FEPLL), attains a dramatic speed-up of two orders of magnitude over EPLL while incurring a negligible drop in the restored image quality (less than 0.5 dB). We demonstrate the efficacy and versatility of our algorithm on a number of inverse problems such as denoising, deblurring, super-resolution, inpainting and devignetting. To the best of our knowledge, FEPLL is the first algorithm that can competitively restore a 512x512 pixel image in under 0.5s for all the degradations mentioned above without specialized code optimizations such as CPU parallelization or GPU implementation.
Image denoising with generalized Gaussian mixture model patch priors,
Charles-Alban Deledalle, Shibin Parameswaran, Truong Q. Nguyen
SIAM Journal on Imaging Sciences, vol. 11, no. 4, pp. 2568-2609, 2018 (epubs SIAM, HAL, ArXiv)
Presented at LIRMM Seminar, Jan 2019, Montpellier, France (slides)
Patch priors have become an important component of image restoration. A powerful approach in this category of restoration algorithms is the popular Expected Patch Log-likelihood (EPLL) algorithm. EPLL uses a Gaussian mixture model (GMM) prior learned on clean image patches as a way to regularize degraded patches. In this paper, we show that a generalized Gaussian mixture model (GGMM) captures the underlying distribution of patches better than a GMM. Even though GGMM is a powerful prior to combine with EPLL, the non-Gaussianity of its components presents major challenges to be applied to a computationally intensive process of image restoration. Specifically, each patch has to undergo a patch classification step and a shrinkage step. These two steps can be efficiently solved with a GMM prior but are computationally impractical when using a GGMM prior. In this paper, we provide approximations and computational recipes for fast evaluation of these two steps, so that EPLL can embed a GGMM prior on an image with more than tens of thousands of patches. Our main contribution is to analyze the accuracy of our approximations based on thorough theoretical analysis. Our evaluations indicate that the GGMM prior is consistently a better fit for modeling image patch distribution and performs better on average in image denoising task.
24. Generalized SURE for optimal shrinkage of singular values in low-rank matrix denoising,
Jérémie Bigot, Charles Deledalle, Delphine Féral
Journal of Machine Learning Research, vol. 18, no. 137, pp. 1-50, 2017 (JMLR, ArXiv)
Presented at ISNPS'2018, June, Salerno, Italy (slides)
We consider the problem of estimating a low-rank signal matrix from noisy measurements under the assumption that the distribution of the data matrix belongs to an exponential family. In this setting, we derive generalized Stein's unbiased risk estimation (SURE) formulas that hold for any spectral estimators which shrink or threshold the singular values of the data matrix. This leads to new data-driven shrinkage rules, whose optimality is discussed using tools from random matrix theory and through numerical experiments. Our approach is compared to recent results on asymptotically optimal shrinking rules for Gaussian noise. It also leads to new procedures for singular values shrinkage in matrix denoising for Poisson-distributed or Gamma-distributed measurements.

[All]  [Scholar Google]


UCSD ECE Best Lecturer Award.
For my graduate classes on Machine Learning and Image Processing.
IEEE GRSS 2016 Transaction Prize Paper Award
for the paper "NL-SAR: a unified Non-Local framework for resolution-preserving (Pol)(In)SAR denoising",
Authors: Charles-Alban Deledalle, Loïc Denis, Florence Tupin, Andreas Reigber and Marc Jäger.
2013 CNRS Bonus for Scientific Excellence
for my works on "Inference and consideration of complex and varied natural image models for restoration purposes"
2011 PhD award in Signal, Image and Vision. Club EEA / GdR ISIS / GRETSI
for the thesis "Image denoising beyond additive Gaussian noise - Patch-based estimators and their application to SAR imagery"
Best student paper award IEEE ICIP 2010
for the paper "Poisson NL Means: Unsupervised Non Local Means for Poison Noise",
Authors: Charles-Alban Deledalle, Loïc Denis and Florence Tupin.

Seminars, invited speakers and workshops:

Oct. 2019ECE Seminar, SDSU, California, 2019Host: C. Mi
Jan. 2019LIRMM Seminar, Université de Montpellier, France, 2019Host: N. Faraj
June 2017TII Seminar, Télécom ParisTech, France, 2017Host: F. Tupin
Feb. 2017UCSD ECE Seminar, San Diego, USA, 2017Host: T. Nguyen
Oct. 2016SPOC Seminar, Université de Bourgogne, Dijon, France, 2016Host: S. Vaiter
May 2016SIAM Conf. on Imaging Science, Albuquerque, USAHost: C. Schönlieb & A. Langer
May 2016SAR Seminar, Télécom ParisTech, France, 2016Host: F. Tupin
July 2015Full-day tutorial at IGARSS, Milano, Italy, 2015
Aug. 2014Seminar SCIL, Université de Sherbrooke, CanadaHost: M. Descoteaux
July 2014Summerschool TUM/DLR, Ftan, SwitzerlandHost: R. Bamler
May 2014SIAM Conference on Imaging Science, Hong-KongHost: R. Willett & R. Giryes
April 2014Seminar Cluster CPU/LaBEX COTE, Bordeeaux, FranceHost: A.-L. Bué
March 2014Seminaires Patchs, Telecom ParisTech, FranceHost: A. Almansa
March 2014Journée SIERRA, Saint-Etienne, FranceHost: J. Debayle
Feb. 2014Salon Aquitec - Stand CNRS Parc des expositions de Bordeaux Lac
Jan. 2014Seminar CESBIO, Toulouse, FranceHost: Y. Kerr
Nov. 2013Journées Télédétection PEPS WAVE, Bordeaux, FranceHost: L. Bombrun
Oct. 2013Atelier ForM@ter MDIS, Autrans, FranceHost: M.-P. Doin
Sept. 2013Conférence GRETSI, Session Plénière, Brest, FranceHost: GRETSI
March 2013Groupe de Travail Image, Bordeaux, FranceHost: P. Coupé
Fév. 2013Salon Aquitec - Stand CNRS Parc des expositions de Bordeaux Lac
Fév. 2013Séminaire TSI Télécom ParisTech Hôte : I. Bloch
Déc. 2012GdR ISIS "Modèles de textures" Télécom ParisTech Hôtes : J-F. Aujol & Y. Gousseau
Oct. 2012Journée FRUMAM Univ Aix-Marseilles Hôte : F-X. Dupé & C. Melot
Juil. 2012Workshop ANR NatImages Nice Hôte : J. Bobin
Mars 2012Séminaire LATP Univ. Aix-Marseilles Hôte : F-X. Dupé
Mars 2012Séminaire I3S Univ. Nice Sofia-Antipolis Hôte : L. Blanc-Féraud
Fév. 2012Séminaire LAGIS École Centrale de Lille Hôte : P. Chainais
Fév. 2012Séminaire IMB/LaBRI/IMS Univ. Bordeaux 1 Hôte : C. Dossal
Juin 2011Séminaire GREYC ENSICAEN, Caen Hôte : L. Condat
Mai 2011Congrès SMAI Guidel, Bretagne Hôtes : J. Delon & C. Louchet
Sept. 2010Séminaire Observatoire de Lyon Univ. Lyon 1 Hôte : E. Thiébaut
Mai 2010Séminaire IETR Univ. Rennes 1 Hôte : E. Pottier

Last modified: Tue Mar 10 03:48:09 Europe/Berlin 2020