Charles Deledalle's Résumé

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Charles Deledalle picture




Telecom ParisTech
Utrecht University


Telecom ParisTech



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."

[See all my publications]  [See my publications on Scholar Google]
Some of my papers in refereed journals
NL-SAR: a unified Non-Local framework for resolution-preserving (Pol)(In)SAR denoising,
Charles-Alban Deledalle, Loïc Denis, Florence Tupin, Andreas Reigber and Marc Jäger
IEEE Trans. on Geoscience and Remote Sensing, vol. 53, no. 4, pp. 2021-2038, 2015 (IEEE Xplore, HAL)
Speckle noise is an inherent problem in coherent imaging systems like synthetic aperture radar. It creates strong intensity fluctuations and hampers the analysis of images and the estimation of local radiometric, polarimetric or interferometric properties. SAR processing chains thus often include a multi-looking (i.e., averaging) filter for speckle reduction, at the expense of a strong resolution loss. Preservation of point-like and fine structures and textures requires to locally adapt the estimation. Non-local means successfully adapt smoothing by deriving data-driven weights from the similarity between small image patches. The generalization of non-local approaches offers a flexible framework for resolution-preserving speckle reduction. We describe a general method, NL-SAR, that builds extended non-local neighborhoods for denoising amplitude, polarimetric and/or interferometric SAR images. These neighborhoods are defined on the basis of pixel similarity as evaluated by multi-channel comparison of patches. Several non-local estimations are performed and the best one is locally selected to form a single restored image with good preservation of radar structures and discontinuities. The proposed method is fully automatic and handles single and multi-look images, with or without interferometric or polarimetric channels. Efficient speckle reduction with very good resolution preservation is demonstrated both on numerical experiments using simulated data and airborne radar images. The source code of a parallel implementation of NL-SAR is released with the paper.
Stein Unbiased GrAdient estimator of the Risk (SUGAR) for multiple parameter selection,
C.-A. Deledalle, S. Vaiter, J.-M. Fadili, G. Peyré
SIAM Journal on Imaging Sciences, vol. 7., no. 4, pp. 2448-2487, 2014 (epubs SIAM, ArXiv)
Algorithms to solve variational regularization of ill-posed inverse problems usually involve operators that depend on a collection of continuous parameters. When these operators enjoy some (local) regularity, these parameters can be selected using the so-called Stein Unbiased Risk Estimate (SURE). While this selection is usually performed by exhaustive search, we address in this work the problem of using the SURE to efficiently optimize for a collection of continuous parameters of the model. When considering non-smooth regularizers, such as the popular l1-norm corresponding to soft-thresholding mapping, the SURE is a discontinuous function of the parameters preventing the use of gradient descent optimization techniques. Instead, we focus on an approximation of the SURE based on finite differences as proposed in (Ramani et al., 2008). Under mild assumptions on the estimation mapping, we show that this approximation is a weakly differentiable function of the parameters and its weak gradient, coined the Stein Unbiased GrAdient estimator of the Risk (SUGAR), provides an asymptotically (with respect to the data dimension) unbiased estimate of the gradient of the risk. Moreover, in the particular case of soft-thresholding, the SUGAR is proved to be also a consistent estimator. The SUGAR can then be used as a basis to perform a quasi-Newton optimization. The computation of the SUGAR relies on the closed-form (weak) differentiation of the non-smooth function. We provide its expression for a large class of iterative proximal splitting methods and apply our strategy to regularizations involving non-smooth convex structured penalties. Illustrations on various image restoration and matrix completion problems are given.
Adaptive regularization of the NL-means: Application to image and video denoising,
Camille Sutour, Charles-Alban Deledalle, Jean-François Aujol
IEEE Trans. on Image Processing, vol. 23, no. 8, pp. 3506-3521, 2014 (IEEE Xplore, HAL)
Image denoising is a central problem in image processing and it is often a necessary step prior to higher level analysis such as segmentation, reconstruction or super-resolution. The non-local means (NL-means) perform denoising by exploiting the natural redundancy of patterns inside an image; they perform a weighted average of pixels whose neighborhoods (patches) are close to each other. This reduces significantly the noise while preserving most of the image content. While it performs well on flat areas and textures, it suffers from two opposite drawbacks: it might over-smooth low-contrasted areas or leave a residual noise around edges and singular structures. Denoising can also be performed by total variation minimization -- the ROF model -- which leads to restore regular images, but it is prone to over-smooth textures, staircasing effects, and contrast losses. We introduce in this paper a variational approach that corrects the over-smoothing and reduces the residual noise of the NL-means by adaptively regularizing non-local methods with the total variation. The proposed regularized NL-means algorithm combines these methods and reduces both of their respective defaults by minimizing an adaptive total variation with a non-local data fidelity term. Besides, this model adapts to different noise statistics and a fast solution can be obtained in the general case of the exponential family. We develop this model for image denoising and we adapt it to video denoising with 3D patches.
Local Behavior of Sparse Analysis Regularization: Applications to Risk Estimation,
Samuel Vaiter, Charles-Alban Deledalle, Gabriel Peyré, Charles Dossal, Jalal Fadili
Applied and Computational Harmonic Analysis, vol. 35, no. 3, pp. 433-451, 2013 (HAL, Science Direct (Elsevier))
In this paper, we aim at recovering an unknown signal x0 from noisy L1measurements y=Phi*x0+w, where Phi is an ill-conditioned or singular linear operator and w accounts for some noise. To regularize such an ill-posed inverse problem, we impose an analysis sparsity prior. More precisely, the recovery is cast as a convex optimization program where the objective is the sum of a quadratic data fidelity term and a regularization term formed of the L1-norm of the correlations between the sought after signal and atoms in a given (generally overcomplete) dictionary. The L1-sparsity analysis prior is weighted by a regularization parameter lambda>0. In this paper, we prove that any minimizers of this problem is a piecewise-affine function of the observations y and the regularization parameter lambda. As a byproduct, we exploit these properties to get an objectively guided choice of lambda. In particular, we develop an extension of the Generalized Stein Unbiased Risk Estimator (GSURE) and show that it is an unbiased and reliable estimator of an appropriately defined risk. The latter encompasses special cases such as the prediction risk, the projection risk and the estimation risk. We apply these risk estimators to the special case of L1-sparsity analysis regularization. We also discuss implementation issues and propose fast algorithms to solve the L1 analysis minimization problem and to compute the associated GSURE. We finally illustrate the applicability of our framework to parameter(s) selection on several imaging problems.
Some of my conference papers
Stein COnsistent Risk Estimator (SCORE) for hard thresholding,
Charles-Alban Deledalle, Gabriel Peyré, Jalal Fadili
SPARS, Lausanne, Switzerland, July 2013 (HAL, poster)
In this work, we construct a risk estimator for hard thresholding which can be used as a basis to solve the difficult task of automatically selecting the threshold. As hard thresholding is not even continuous, Stein's lemma cannot be used to get an unbiased estimator of degrees of freedom, hence of the risk. We prove that under a mild condition, our estimator of the degrees of freedom, although biased, is consistent. Numerical evidence shows that our estimator outperforms another biased risk estimator.
Image denoising with patch based PCA: local versus global,
Charles-Alban Deledalle, Joseph Salmon, Arnak Dalalyan
In the proceedings of BMVC, University of Dundee, August-Septembre 2011 (pdf, slides)
In recent years, overcomplete dictionaries combined with sparse learning techniques became extremely popular in computer vision. While their usefulness is undeniable, the improvement they provide in specific tasks of computer vision is still poorly understood. The aim of the present work is to demonstrate that for the task of image denoising, nearly state-of-the-art results can be achieved using orthogonal dictionaries only, provided that they are learned directly from the noisy image. To this end, we introduce three patch-based denoising algorithms which perform hard thresholding on the coefficients of the patches in image-specific orthogonal dictionaries. The algorithms differ by the methodology of learning the dictionary: local PCA, hierarchical PCA and global PCA. We carry out a comprehensive empirical evaluation of the performance of these algorithms in terms of accuracy and running times. The results reveal that, despite its simplicity, PCA-based denoising appears to be competitive with the state-of-the-art denoising algorithms, especially for large images and moderate signal-to-noise ratios.
Poisson NL means: unsupervised non local means for Poisson noise,
Charles-Alban Deledalle, Florence Tupin and Loïc Denis
In the proceedings of ICIP, Hong Kong, September 2010 (pdf, slides)
Best student paper award IEEE ICIP 2010
An extension of the non local (NL) means is proposed for images damaged by Poisson noise. The proposed method is guided by the noisy image and a pre-filtered image and is adapted to the statistics of Poisson noise. The influence of both images can be tuned using two filtering parameters. We propose an automatic setting to select these parameters based on the minimization of the estimated risk (mean square error). This selection uses an estimator of the MSE for NL means with Poisson noise and Newton's method to find the optimal parameters in few iterations.
My PhD
Image denoising beyond additive Gaussian noise
Patch-based estimators and their application to SAR imagery
Charles-Alban Deledalle
In Telecom ParisTech, France, Nov 15, 2011 (HAL, slides)
Noise in images often limits visual and automatic interpretation of the scene. Speckle in synthetic aperture radar (SAR) imagery and shot noise in photon-limited imagery are two examples of strong corruptions that require the use of denoising techniques. Patches are small image parts that capture both textures and local structures. Though being crude low-level features (compared to higher level descriptors), they have led to very powerful image processing approaches by exploiting the natural redundancy of images. Patch-based methods achieve state-of-the-art denoising performance. The classical patch-based denoising technique non-local (NL) means is designed for images corrupted by an additive Gaussian noise (i.e., fluctuations being symmetrical, signal-independent without outliers). NL means cannot be applied directly on images corrupted by a non-Gaussian process especially with non-symmetrical distribution, signal-dependence and heavy-tail such as speckle and shot noise. The goal of this thesis is to bridge the gap between patch-based denoising methods restricted to Gaussian noise and techniques dedicated to SAR despeckling. After reviewing image denoising techniques for Gaussian noise and for non-Gaussian noise, we propose an extension of the NL means that adapts to a given noise distribution. Besides the problem of image denoising, we study the problem of patch comparison under non-Gaussian conditions. Many tasks in computer vision require matching image parts. We introduce a similarity criterion grounded on the generalized likelihood ratio test and illustrate its effectiveness on different applications including detection, stereo-vision and motion-tracking. This criterion is at the heart of the proposed patch-based estimator. An iterative scheme is proposed to deal with strong noise corruptions and we develop an unsupervised method for parameter setting. Our approach leads to state-of-the-art denoising results in SAR imagery for amplitude images, as well as interferometric or polarimetric data. The proposed technique is applied successfully to one of the latest aerial SAR sensor: F-SAR from the German Aerospace Center (DLR). Images with strong contrasts suffer from denoising artefacts known as noise halo due to the absence of similar patches in the vicinity of some structures. This residual noise can be reduced by considering patches with shapes of various scales and orientations. Local selection of relevant shapes leads to an improved denoising quality, especially close to edges.

[See all my publications]  [See my publications on Scholar Google]


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 Award 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:

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. Willet & 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

Peer reviews

Refereeing of the work of peers as an anonymous reviewer for:
In France (currently out of this office):
Charles Deledalle - charles-alban (dot) deledalle (at) math.u-bordeaux (dot) fr
Bureau 209
Institut de Mathématiques de Bordeaux
Université Bordeaux
351, cours de la Libération - F-33405 TALENCE cedex
+33 (0)5 40 00 21 14
In USA (currently at this office):
Charles Deledalle - cdeledalle (at) ucsd (dot) edu
Jacobs Hall, Room 4808
Jacobs School of Engineering
University of California, San Diego
9500 Gilman Drive
La Jolla, CA 92093
Last modified: Mon Jan 9 22:50:33 Europe/Berlin 2017