Charles Deledalle picture
 
[english][français]

Charles Deledalle

Chargé 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
 
my contact information
 

Biographie

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


Évènements


Travaux de recherche

Algorithme NL means

Publications récentes

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."


[Voir toutes mes publications]  [See my publications on Scholar Google]
Extrait de mes articles de revues
NL-SAR: a unified Non-Local framework for resolution-preserving (Pol)(In)SAR denoising,
Charles-Alban Deledalle, Loïc Denis, Florence Tupin, Andreas Reigber et 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.
 
Extrait de mes actes de conférences
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 et 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.
 
Doctorat
Débruitage d'images au-delà du bruit additif gaussien
Estimateurs à patchs et leur application à l'imagerie SAR
,
Charles-Alban Deledalle
In Telecom ParisTech, France, Nov 15, 2011 (HAL, slides)
 
Le bruit dans les images limite souvent l'interprétation visuelle ou automatique de la scène. Le chatoiement ou speckle en imagerie radar à synthèse d'ouverture (RSO) et le bruit de grenaille ou shot noise en imagerie à faible luminosité sont deux exemples de fortes corruptions qui nécessitent l'utilisation de techniques de débruitage. Les vignettes ou patchs sont de petites imagettes qui capturent à la fois les textures et les structures locales. Bien qu'étant assez rudimentaires (comparées à des descripteurs de plus haut niveau), elles ont mené à de puissantes approches de traitement d'images tirant parti de la redondance naturelle des images. Les méthodes à patchs représentent l'état-de-l'art des méthodes de débruitage. La technique classique de débruitage à patchs, les moyennes non-locales (NL), est conçue pour les images corrompues par du bruit additif gaussien (c-à-d., pour des fluctuations symétriques, indépendantes du signal et sans valeurs extrêmes). Les moyennes NL ne peuvent pas être appliquées directement sur des images corrompues par un bruit non-gaussien surtout pour des distributions asymétriques, dépendantes du signal et à queues lourdes telles que le bruit de chatoiement et le bruit de grenaille. Le but de cette thèse est de combler le fossé entre les méthodes de débruitage à patchs, restreintes au bruit gaussien, et les techniques dédiées aux images RSO. Après avoir examiné les techniques de débruitage d'image pour le bruit gaussien puis non-gaussien, nous proposons une extension des moyennes NL qui s'adapte à la distribution d'un bruit donné. Au-delà du problème du débruitage d'image, nous étudions le problème de la comparaison de patchs sous conditions non-gaussiennes. La plupart des tâches de vision par ordinateur requièrent de mettre en correspondance des parties d'images. Nous introduisons un critère de similarité fondé sur le rapport de vraisemblance généralisé et nous illustrons son efficacité sur différentes applications dont la détection, la vision stéréoscopique et le suivi de mouvement. Ce critère est au coeur de l'estimateur à patchs proposé. Un schéma itératif est élaboré pour faire face aux fortes corruptions de bruit et nous développons une méthode non-supervisée pour le réglage des paramètres. Notre approche mène à des résultats de débruitage état-de-l'art en imagerie RSO pour les images d'amplitude, ainsi que les données interférométriques ou polarimétriques. La technique proposée est appliquée avec succès sur l'un des derniers capteurs aérien RSO: F-SAR de l'agence aérospatiale allemande (DLR). Les images avec de forts contrastes souffrent d'un artéfact de débruitage de type halo de bruit dû à l'absence de patchs similaires dans les environs de certaines structures. Ce bruit résiduel peut être réduit en considérant des patchs avec des formes d'échelle et d'orientation variées. La sélection locale des formes pertinentes permet d'améliorer la qualité du débruitage, surtout à proximité des contours.

[Voir toutes mes publications]  [See my publications on Scholar Google]
 

Logiciels

Problèmes inverses et matrices de rang faible
GSURE low rank
GSURE in low rank matrix denoising (2017)
Matlab open-source software distributed under CeCILL license for data driven srhinkage of singular values.
 
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 spectral estimators, whose optimality is discussed using tools from random matrix theory and through numerical experiments. Under the spiked population model and in the asymptotic setting where the dimensions of the data matrix are let going to infinity, some theoretical properties of our approach are compared to recent results on asymptotically optimal shrinking rules for Gaussian noise. It also leads to new procedures for singular values shrinkage in finite-dimensional matrix denoising for Gamma-distributed and Poisson-distributed measurements.
 
SUGAR software
SUGAR (2014)
Matlab open-source software for the automatic selection of (multiple) parameters in inverse problems.
 
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.
 
Débruitage
MuLoG software
MuLoG (2016-2017)
Matlab open-source software distributed under CeCILL license to perform (Pol)(In)SAR filtering with embedded Gaussian denoiser.

 
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) called 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 algorithm 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.
 
RNLF software
RNLF: Regularized NL-means (RNL) and Noise Level Function (NLF) estimation (2015)
Matlab open-source software to perform (blind) denoising. It implements the followings
  • Generation of several types of signal-dependent noises,
  • Homegeneous block detection insensitive to the noise model,
  • Estimation of signal-dependent noise with 2nd order polynomial variance,
  • Non-local, total-variation and regularized non-local filtering for signal-dependent noises.
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. When the nature of the noise is unknown, a two-step algorithm automatically estimates the noise level function of stationary noise from a single image, i.e., the noise variance as a function of the image intensity. First, the image is divided into small square regions and a non-parametric test is applied to decide weather each region is homogeneous or not. Based on Kendall's τ coefficient (a rank-based measure of correlation), this detector has a non-detection rate independent on the unknown distribution of the noise, provided that it is at least spatially uncorrelated. Once homogeneous regions are detected, the noise level function is estimated as a second order polynomial minimizing the l1 error on the statistics of these regions. Given the known or estimated nature of noise, the non-local means (NL-means) can be used to perform denoising by exploiting the natural redundancy of patterns inside an image. Denoising can also be performed by total variation (TV) minimization which leads to restore regular images. The proposed regularized NL-means (RNL) algorithm combines these two 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 leading to a fully blind denoising algorithm. We develop this model for image denoising and video denoising with 3D patches.
 
NL-SAR software
NL-SAR (2013-2014)
Open-source software distributed under CeCILL license to perform adaptive non-local (Pol)(In)SAR filtering.
Interface in command line, IDL, Matlab, Python and C dynamic library.
Plug in for PolSARpro.
 
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. NL-SAR is a general method 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 can handle single and multi-look images, with or without interferometric or polarimetric channels. Efficient speckle reduction with very good resolution preservation has been demonstrated both on numerical experiments using simulated data and airborne radar images.
 
NLSPCA software
Poisson NLSPCA (2012)
Matlab open-source software to perform non-local filtering in an extended PCA domain for Poisson noise.
 
Photon-limited imaging arises when the number of photons collected by a sensor array is small relative to the number of detector elements. Photon limitations are an important concern for many applications such as spectral imaging, night vision, nuclear medicine, and astronomy. Typically a Poisson distribution is used to model these observations, and the inherent heteroscedasticity of the data combined with standard noise removal methods yields significant artifacts. A novel denoising algorithm is implemented for photon-limited images which combines elements of dictionary learning and sparse patch-based representations of images. The method employs both an adaptation of Principal Component Analysis (PCA) for Poisson noise and recently developed sparsity-regularized convex optimization algorithms for photon-limited images. A comprehensive empirical evaluation of the proposed method helps characterize the performance of this approach relative to other state-of-the-art denois ing methods. The results reveal that, despite its conceptual simplicity, Poisson PCA-based denoising appears to be highly competitive in very low light regimes.
 
NL-PCA software
NLPCA (2011)
Matlab open-source software to perform non-local filtering in the PCA domain.
 
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 method- ology 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, espe- cially for large images and moderate signal-to-noise ratios.
 
NLMSAP software
NLMSAP (2011)
Matlab open-source software to perform non-local filtering with shape adaptive patches.
 
This implements an extension of the Non-Local Means (NL-Means) denoising algorithm. The idea is to replace the usual square patches used to compare pixel neighborhoods with various shapes that can take advantage of the local geometry of the image. We provide a fast algorithm to compute the NL-Means with arbitrary shapes thanks to the Fast Fourier Transform. We then consider local combinations of the estimators associated with various shapes by using Stein’s Unbiased Risk Estimate (SURE). Experimental results show that this algorithm improve the standard NL-Means performance and is close to state-of-the-art methods, both in terms of visual quality and numerical results. Moreover, common visual artifacts usually observed by denoising with NL-Means are reduced or suppressed thanks to our approach.
 
Poisson NL-means software
Poisson NL-means (2010)
Matlab/Mex software to perform non-local filtering for Poisson noise with automatic selection of the denoising parameters.
 
This work has been achieved by Charles Deledalle supervised by Florence Tupin and Loïc Denis. The aim was to adapt the Non-Local means (NL means) filter [1] to images sensed in low-light conditions. The Poisson NL means filter is based on the PPB filter [2] which ables to extend the NL means to deal with the Poisson distribution followed by the noise in such images. An efficient estimator has been designed, able to cope with the statistics and especially with the signal-dependent nature of such images. The Poisson NL means filter is an an extension of the non local (NL) [1] means 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 as recommended in [2]. The influence of both images can be tuned using two filtering parameters. These two parameters are automatically set to minimize an estimation of the mean square error (MSE). This selection uses an estimator of the MSE for NL means with Poisson noise and a Newton's method to find the optimal parameters in few iterations.
 
NL-InSAR software
Non-local InSAR (NL-InSAR) filter (2011)
Matlab/Mex software of the PPB version for SAR interferometry.
 
This work has been achieved by Charles Deledalle supervised by Florence Tupin and Loïc Denis. The aim was to adapt the Non-Local means (NL means) filter [7] to InSAR images. The NL-InSAR filter is based on the PPB filter [6] which is an extension of the NL means to non-gaussian noise and multivariate data. Then, an efficient estimator as been designed, able to cope with the statistical nature and the multi-dimensionnality of InSAR images. Interferometric synthetic aperture radar (InSAR) data provides reflectivity, interferometric phase and coherence images, which are paramount to scene interpretation or low-level processing tasks such as segmentation and 3D reconstruction. These images are estimated in practice from hermitian product on local windows. These windows lead to biases and resolution losses due to local heterogeneity caused by edges and textures. We propose a non-local approach for the joint estimation of the reflectivity, the interferometric phase and the coherence images from an interferometric pair of co-registered single-look complex (SLC) SAR images. Non-local techniques are known to efficiently reduce noise while preserving structures by performing a weighted averaging of similar pixels. Two pixels are considered similar if the surrounding image patches are "resembling". Patch- similarity is usually defined as the Euclidean distance between the vectors of graylevels. A statistically grounded patch-similarity criterion suitable to SLC images is derived. A weighted maximum likelihood estimation of the SAR interferogram is then computed with weights derived in a data-driven way. Weights are defined from intensity and interferometric phase, and are iteratively refined based both on the similarity between noisy patches and on the similarity of patches from the previous estimate..
 
PPB software
Probabilistic Patch Based (PPB) filter (2009)
Matlab/Mex software to perform iterative non-local filtering for reducing: additive white Gaussian noise or, multiplicative speckle noise, i.e Nakagami-Rayleigh distributions (NL-SAR).
 
This work has been achieved by Charles Deledalle supervised by Florence Tupin and Loïc Denis. The aim was to adapt the Non-Local means (NL means) filter [2] to SAR images. Then, an efficient filter as been designed, able to cope with non Gaussian noise, multi-dimensionnal images and especially to the various existing SAR images. Results on the extended filter for amplitude SAR images are given on this page. The NL-InSAR filter is also an extension of the non-local means based on the PPB filter for interferometric SAR images, as well as the Poisson NL means filter for images sensed in low-light conditions.
 
Edition
MooseTeX
MooseTeX (2012 - 2014)
Open-source software distributed under CeCILL license for UNIX-like systems (such as Linux and MacOS-X).
 
MooseTeX helps you generate high quality LaTeX documents of any kind such as articles, letters, reports, theses, presentations or posters. Based on the technology of Makefile(s), the purpose of MooseTeX is ``to determine automatically which pieces of a (large) LaTeX project need to be recompiled, and issue the commands to recompile them''. For doing so, MooseTeX also includes a suite of tools to recompile each of such pieces. Note that MooseTeX is non-intrusive. It does not change the way you use LaTeX and is, as a consequence, compatible with your older projects. You can also use MooseTeX within collaborative LaTeX projects without imposing the use of MooseTeX to other collaborators.
 

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
FRANCE
+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
USA
 
Last modified: Sun Apr 23 04:17:32 Europe/Berlin 2017
Ce site utilise Google Analytics [en savoir plus]