90 results on '"Deledalle, Charles-Alban"'
Search Results
2. Block-Based Refitting in ℓ12 Sparse Regularization
- Author
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Deledalle, Charles-Alban, Papadakis, Nicolas, Salmon, Joseph, and Vaiter, Samuel
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- 2021
- Full Text
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3. NORCAMA: Change analysis in SAR time series by likelihood ratio change matrix clustering
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Su, Xin, Deledalle, Charles-Alban, Tupin, Florence, and Sun, Hong
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- 2015
- Full Text
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4. Local behavior of sparse analysis regularization: Applications to risk estimation
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Vaiter, Samuel, Deledalle, Charles-Alban, Peyré, Gabriel, Dossal, Charles, and Fadili, Jalal
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- 2013
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5. Edge-Based Multi-modal Registration and Application for Night Vision Devices
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Sutour, Camille, Aujol, Jean-François, Deledalle, Charles-Alban, and de Senneville, Baudouin Denis
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- 2015
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6. Poisson Noise Reduction with Non-local PCA
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Salmon, Joseph, Harmany, Zachary, Deledalle, Charles-Alban, and Willett, Rebecca
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- 2014
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7. Supervised classification of solar features using prior information
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De Visscher Ruben, Delouille Véronique, Dupont Pierre, and Deledalle Charles-Alban
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Solar image processing ,Corona ,Statistics and probability ,Classification ,Meteorology. Climatology ,QC851-999 - Abstract
Context: The Sun as seen by Extreme Ultraviolet (EUV) telescopes exhibits a variety of large-scale structures. Of particular interest for space-weather applications is the extraction of active regions (AR) and coronal holes (CH). The next generation of GOES-R satellites will provide continuous monitoring of the solar corona in six EUV bandpasses that are similar to the ones provided by the SDO-AIA EUV telescope since May 2010. Supervised segmentations of EUV images that are consistent with manual segmentations by for example space-weather forecasters help in extracting useful information from the raw data. Aims: We present a supervised segmentation method that is based on the Maximum A Posteriori rule. Our method allows integrating both manually segmented images as well as other type of information. It is applied on SDO-AIA images to segment them into AR, CH, and the remaining Quiet Sun (QS) part. Methods: A Bayesian classifier is applied on training masks provided by the user. The noise structure in EUV images is non-trivial, and this suggests the use of a non-parametric kernel density estimator to fit the intensity distribution within each class. Under the Naive Bayes assumption we can add information such as latitude distribution and total coverage of each class in a consistent manner. Those information can be prescribed by an expert or estimated with an Expectation-Maximization algorithm. Results: The segmentation masks are in line with the training masks given as input and show consistency over time. Introduction of additional information besides pixel intensity improves upon the quality of the final segmentation. Conclusions: Such a tool can aid in building automated segmentations that are consistent with some ground truth’ defined by the users.
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- 2015
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8. How to Compare Noisy Patches? Patch Similarity Beyond Gaussian Noise
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Deledalle, Charles-Alban, Denis, Loïc, and Tupin, Florence
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- 2012
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9. Non-local Methods with Shape-Adaptive Patches (NLM-SAP)
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Deledalle, Charles-Alban, Duval, Vincent, and Salmon, Joseph
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- 2012
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10. resolution preserving speckle reduction of SAR images: the benefits of speckle decorrelation and target extraction
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Abergel, Rémy, Denis, L., Tupin, Florence, Ladjal, Saïd, Deledalle, Charles-Alban, Almansa, Andrés, Mathématiques Appliquées Paris 5 (MAP5 - UMR 8145), Université Paris Descartes - Paris 5 (UPD5)-Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS), Image, Modélisation, Analyse, GEométrie, Synthèse (IMAGES), Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT), Département Images, Données, Signal (IDS), Télécom ParisTech, Laboratoire Hubert Curien [Saint Etienne] (LHC), Université Jean Monnet [Saint-Étienne] (UJM)-Centre National de la Recherche Scientifique (CNRS)-Institut d'Optique Graduate School (IOGS), Institut de Mathématiques de Bordeaux (IMB), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)
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[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] ,ComputingMilieux_MISCELLANEOUS - Abstract
International audience
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- 2019
11. Refitting solutions promoted by $\ell_{12}$ sparse analysis regularization with block penalties
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DELEDALLE, Charles-Alban, PAPADAKIS, Nicolas, SALMON, Joseph, VAITER, Samuel, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM), Institut de Mathématiques de Bourgogne [Dijon] (IMB), Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université de Bourgogne (UB), ANR-16-CE33-0010,GOTMI,Generalized Optimal Transport Models for Image processing(2016), European Project: 777826,NoMADS(2018), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université de Bourgogne (UB)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS), Papadakis, Nicolas, Generalized Optimal Transport Models for Image processing - - GOTMI2016 - ANR-16-CE33-0010 - AAPG2016 - VALID, and Nonlocal Methods for Arbitrary Data Sources - NoMADS - 2018-03-01 - 2022-02-28 - 777826 - VALID
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[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing ,[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing ,Optimization and Control (math.OC) ,Image and Video Processing (eess.IV) ,FOS: Mathematics ,FOS: Electrical engineering, electronic engineering, information engineering ,Electrical Engineering and Systems Science - Image and Video Processing ,Mathematics - Optimization and Control - Abstract
International audience; In inverse problems, the use of an $\ell_{12}$ analysis regularizer induces a bias in the estimated solution. We propose a general refitting framework for removing this artifact while keeping information of interest contained in the biased solution. This is done through the use of refitting block penalties that only act on the co-support of the estimation. Based on an analysis of related works in the literature, we propose a new penalty that is well suited for refitting purposes. We also present an efficient algorithmic method to obtain the refitted solution along with the original (biased) solution for any convex refitting block penalty. Experiments illustrate the good behavior of the proposed block penalty for refitting.
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- 2019
12. Accelerating GMM-Based Patch Priors for Image Restoration: Three Ingredients for a 100x Speed-Up
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Parameswaran, Shibin, Deledalle, Charles-Alban, Denis, Loïc, Nguyen, Truong, University of California [San Diego] (UC San Diego), University of California, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Hubert Curien [Saint Etienne] (LHC), and Université Jean Monnet [Saint-Étienne] (UJM)-Centre National de la Recherche Scientifique (CNRS)-Institut d'Optique Graduate School (IOGS)
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[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing ,ComputingMilieux_MISCELLANEOUS - Abstract
International audience
- Published
- 2019
13. Ratio-based multi-temporal SAR images denoising
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Zhao , Weiying, Denis , Loïc, Deledalle , Charles-Alban, Maître , Henri, Nicolas , Jean-Marie, Tupin , Florence, LTCI, Télécom ParisTech, Université Paris-Saclay, Laboratoire Hubert Curien [Saint Etienne] ( LHC ), Université Jean Monnet [Saint-Étienne] ( UJM ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de Mathématiques de Bordeaux ( IMB ), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux ( UB ) -Institut Polytechnique de Bordeaux ( Bordeaux INP ) -Centre National de la Recherche Scientifique ( CNRS )
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speckle reduction ,[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing ,Computer Science::Computer Vision and Pattern Recognition ,ratio image ,Index Terms—Multi-temporal SAR series ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,super- image ,SAR - Abstract
—In this paper, we propose a generic multi-temporal SAR despeckling method to extend any single-image speckle reduction algorithm to multi-temporal stacks. Our method, RAtio-BAsed multi-temporal SAR despeckling (RABASAR), is based on ratios and fully exploits a " super-image " (i.e. temporal mean) in the process. The proposed approach can be divided into three steps: 1) calculation of the " super-image " through temporal averaging; 2) denoising the ratio images formed through dividing the noisy images by the " super-image " ; 3) computing denoised images by multiplying the denoised ratio images with the " super-image ". Thanks to the spatial stationarity improvement in the ratio images, denoising these ratio images with a speckle-reduction method is more effective than denoising the original multi-temporal stack. The data volume to be processed is also reduced compared to other methods through the use of the " super-image ". The comparison with several state-of-the-art reference methods shows numerically (peak signal-noise-ratio, structure similarity index) and visually better results both on simulated and real SAR stacks. The proposed ratio-based denoising framework successfully extends single-image SAR denoising methods in order to exploit the temporal information of a time series.
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- 2018
14. Parameter Estimation in Finite Mixture Models by Regularized Optimal Transport: A Unified Framework for Hard and Soft Clustering
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Papadakis, Nicolas, Dessein, Arnaud, Deledalle, Charles-Alban, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Représentations musicales (Repmus), Sciences et Technologies de la Musique et du Son (STMS), Institut de Recherche et Coordination Acoustique/Musique (IRCAM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche et Coordination Acoustique/Musique (IRCAM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), ANR-16-CE33-0010,GOTMI,Generalized Optimal Transport Models for Image processing(2016), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Papadakis, Nicolas, and Generalized Optimal Transport Models for Image processing - - GOTMI2016 - ANR-16-CE33-0010 - AAPG2016 - VALID
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FOS: Computer and information sciences ,Computer Science - Learning ,[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing ,[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing ,Statistics - Machine Learning ,Machine Learning (stat.ML) ,[STAT.OT]Statistics [stat]/Other Statistics [stat.ML] ,[STAT.OT] Statistics [stat]/Other Statistics [stat.ML] ,Machine Learning (cs.LG) - Abstract
In this short paper, we formulate parameter estimation for finite mixture models in the context of discrete optimal transportation with convex regularization. The proposed framework unifies hard and soft clustering methods for general mixture models. It also generalizes the celebrated $k$\nobreakdash-means and expectation-maximization algorithms in relation to associated Bregman divergences when applied to exponential family mixture models.
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- 2017
15. Generalized SURE for optimal shrinkage of singular values in low-rank matrix denoising
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Bigot, Jérémie, Deledalle, Charles-Alban, Féral, Delphine, Institut de Mathématiques de Bordeaux (IMB), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)
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62H12, 62H25 ,singular value decomposition ,Mathematics - Statistics Theory ,Statistics Theory (math.ST) ,random matrix theory ,spectral estimator ,Stein's unbiased risk estimate ,low-rank model ,[STAT]Statistics [stat] ,matrix denoising ,optimal shrinkage rule ,degrees of freedom ,exponential family ,FOS: Mathematics ,[MATH]Mathematics [math] - Abstract
International audience; 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.
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- 2017
16. Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100$\times$ speed-up
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Parameswaran, Shibin, Deledalle, Charles-Alban, Denis, Lo��c, and Nguyen, Truong Q.
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FOS: Computer and information sciences ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition - Abstract
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.
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- 2017
17. Blind atmospheric turbulence deconvolution.
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Deledalle, Charles‐Alban and Gilles, Jérôme
- Abstract
A new blind image deconvolution technique is developed for atmospheric turbulence deblurring to overcome limitations of 'generic' blind deconvolution algorithms that do not take into account the complicated physics of the turbulence. The originality of the proposed approach relies on an actual physical model, known as the Fried kernel, that quantifies the impact of the atmospheric turbulence on the optical resolution of images. While the original expression of the Fried kernel can seem cumbersome at first sight, the authors show that it can be reparameterised in a much simpler form. This simple expression allows to efficiently embed this kernel in the proposed blind atmospheric turbulence deconvolution (BATUD) algorithm. BATUD is an iterative algorithm that alternately performs deconvolution and estimates the Fried kernel by jointly relying on a Gaussian mixture model prior to natural image patches and controlling for the square Euclidean norm of the Fried kernel. Numerical experiments show that the proposed blind deconvolution algorithm behaves well in different simulated turbulence scenarios, as well as on real images. Not only BATUD outperforms state‐of‐the‐art approaches used in atmospheric turbulence deconvolution in terms of image quality metrics but is also faster. [ABSTRACT FROM AUTHOR]
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- 2020
- Full Text
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18. Characterizing the maximum parameter of the total-variation denoising through the pseudo-inverse of the divergence
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Deledalle, Charles-Alban, Papadakis, Nicolas, Salmon, Joseph, Vaiter, Samuel, Institut de Mathématiques de Bordeaux ( IMB ), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux ( UB ) -Institut Polytechnique de Bordeaux ( Bordeaux INP ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire Traitement et Communication de l'Information ( LTCI ), Télécom ParisTech-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ), Institut de Mathématiques de Bourgogne [Dijon] ( IMB ), Université de Bourgogne ( UB ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Institut de Mathématiques de Bourgogne [Dijon] (IMB), Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), and Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université de Bourgogne (UB)
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FOS: Computer and information sciences ,[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing ,[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing ,Statistics - Machine Learning ,[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] ,Regularization ,Pseudo-inverse ,[ INFO.INFO-TI ] Computer Science [cs]/Image Processing ,Machine Learning (stat.ML) ,[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH] ,Total-variation ,[ STAT.TH ] Statistics [stat]/Statistics Theory [stat.TH] ,Divergence - Abstract
International audience; We focus on the maximum regularization parameter for anisotropic total-variation denoising. It corresponds to the minimum value of the regularization parameter above which the solution remains constant. While this value is well know for the Lasso, such a critical value has not been investigated in details for the total-variation. Though, it is of importance when tuning the regularization parameter as it allows fixing an upper-bound on the grid for which the optimal parameter is sought. We establish a closed form expression for the one-dimensional case, as well as an upper-bound for the two-dimensional case, that appears reasonably tight in practice. This problem is directly linked to the computation of the pseudo-inverse of the divergence, which can be quickly obtained by performing convolutions in the Fourier domain.
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- 2017
19. Very high resolution and interferometric SAR: Markovian and patch-based non-local mathematical models
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DELEDALLE, Charles-Alban, DENIS, Loïc, FERRAIOLI, Giampaolo, PASCAZIO, Vito, SCHIRINZI, Gilda, TUPIN, Florence, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Hubert Curien [Saint Etienne] (LHC), Université Jean Monnet [Saint-Étienne] (UJM)-Centre National de la Recherche Scientifique (CNRS)-Institut d'Optique Graduate School (IOGS), Universita degli studi di Napoli 'Parthenope' [Napoli], Laboratoire Traitement et Communication de l'Information (LTCI), and Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)
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Computer Science::Graphics ,[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing ,Physics::Atmospheric and Oceanic Physics - Abstract
International audience; This chapter is dedicated to very high resolution (VHR) SAR imagery, including interferometric applications. First, the principles of SAR data acquisition are presented as well as the different types of configurations. The widely adopted Gaussian complex model of fully developed speckle is described as well as more advanced statistical models for VHR SAR data that account for textures. The following two parts are devoted to SAR image estimation and to image denoising within two different frameworks. First, Markovian modeling is introduced and the associated optimization approaches are presented, including graph-cut based optimization. The second framework is the patch-based non-local modeling of SAR complex data. Both frameworks are adapted to SAR images through the use of statistical models specific to SAR imagery. Their applications to amplitude data, interferometry, and fusion with optical data are illustrated. A special focus is given to phase unwrapping applied to single and multi- channel interferometry, showing the usefulness of local and global contextual models.
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- 2017
20. A Tour of Patch-based Methods and their Applications in Remote Sensing
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Tupin, Florence, Deledalle, Charles-Alban, HAL, TelecomParis, Image, Modélisation, Analyse, GEométrie, Synthèse (IMAGES), Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris-Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Département Images, Données, Signal (IDS), and Télécom ParisTech
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[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV] ,[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] ,ComputingMilieux_MISCELLANEOUS - Abstract
International audience
- Published
- 2017
21. Contrast re-enhancement of Total-Variation regularization jointly with the Douglas-Rachford iterations
- Author
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Deledalle, Charles-Alban, Papadakis, Nicolas, Salmon, Joseph, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Projet exploratoire du GdR ISIS 2014 (ASPIRIM), and Deledalle, Charles-Alban
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[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV] ,[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] - Abstract
International audience; Restoration of a piece-wise constant signal can be performed using anisotropic Total-Variation (TV) regularization. Anisotropic TV may capture well discontinuities but suffers from a systematic loss of contrast. This contrast can be re-enhanced in a post-processing step known as least-square refitting. We propose here to jointly estimate the refitting during the Douglas-Rachford iterations used to produce the original TV result. Numerical simulations show that our technique is more robust than the naive post-processing one.
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- 2015
22. Machine learning in acoustics: Theory and applications.
- Author
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Bianco, Michael J., Gerstoft, Peter, Traer, James, Ozanich, Emma, Roch, Marie A., Gannot, Sharon, and Deledalle, Charles-Alban
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MACHINE learning ,ACOUSTICS ,ARCHITECTURAL acoustics ,EARTH sciences ,MARINE sciences ,SOUND reverberation ,THEORY-practice relationship ,AUTOMATIC speech recognition - Abstract
Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics. ML is a broad family of techniques, which are often based in statistics, for automatically detecting and utilizing patterns in data. Relative to conventional acoustics and signal processing, ML is data-driven. Given sufficient training data, ML can discover complex relationships between features and desired labels or actions, or between features themselves. With large volumes of training data, ML can discover models describing complex acoustic phenomena such as human speech and reverberation. ML in acoustics is rapidly developing with compelling results and significant future promise. We first introduce ML, then highlight ML developments in four acoustics research areas: source localization in speech processing, source localization in ocean acoustics, bioacoustics, and environmental sounds in everyday scenes. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
23. SUPER-RESOLUTION FROM A LOW- AND PARTIAL HIGH- RESOLUTION IMAGE PAIR
- Author
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Hidane, Moncef, Aujol, Jean-François, Berthoumieu, Yannick, Deledalle, Charles-Alban, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de l'intégration, du matériau au système (IMS), Centre National de la Recherche Scientifique (CNRS)-Institut Polytechnique de Bordeaux-Université Sciences et Technologies - Bordeaux 1, and CPU (ANR- 10-IDEX-03-02)
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Computer Science::Computer Vision and Pattern Recognition ,[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] - Abstract
International audience; The classical super-resolution (SR) setting starts with a set of low-resolution (LR) images related by subpixel shifts and tries to reconstruct a single high-resolution (HR) image. In some cases, partial observations about the HR image are also available. Trying to complete the missing HR data without any reference to LR ones is an inpainting (or completion) problem. In this paper, we consider the problem of recov- ering a single HR image from a pair consisting of a complete LR and incomplete HR image pair. This setting arises in par- ticular when one wants to fuse image data captured at two different resolutions. We propose an efficient algorithm that allows to take advantage of both image data by first learning nonlocal interactions from an interpolated version of the LR image using patches. Those interactions are then used by a convex energy function whose minimization yields a super- resolved complete image.
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- 2014
24. Ratio-Based Multitemporal SAR Images Denoising: RABASAR.
- Author
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Zhao, Weiying, Deledalle, Charles-Alban, Denis, Loic, Maitre, Henri, Nicolas, Jean-Marie, and Tupin, Florence
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IMAGE denoising , *SYNTHETIC aperture radar , *STRUCTURE-activity relationships , *FASTING , *TIME series analysis - Abstract
In this paper, we propose a fast and efficient multitemporal despeckling method. The key idea of the proposed approach is the use of the ratio image, provided by the ratio between an image and the temporal mean of the stack. This ratio image is easier to denoise than a single image thanks to its improved stationarity. Besides, temporally stable thin structures are well preserved thanks to the multitemporal mean. The proposed approach can be divided into three steps: 1) estimation of a “superimage” by temporal averaging and possibly spatial denoising; 2) denoising of the ratio between the noisy image of interest and the “superimage”; and 3) computation of the denoised image by remultiplying the denoised ratio by the “superimage.” Because of the improved spatial stationarity of the ratio images, denoising these ratio images with a speckle-reduction method is more effective than denoising images from the original multitemporal stack. The amount of data that is jointly processed is also reduced compared to other methods through the use of the “superimage” that sums up the temporal stack. The comparison with several state-of-the-art reference methods shows better results numerically (peak signal-noise-ratio and structure similarity index) as well as visually on simulated and synthetic aperture radar (SAR) time series. The proposed ratio-based denoising framework successfully extends single-image SAR denoising methods to time series by exploiting the persistence of many geometrical structures. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
25. Denoising based on non-local means for ultrasound images with simultaneous multiple noise distributions
- Author
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Salvadeo, Denis, Bloch, Isabelle, Tupin, Florence, Mascarenhas, Nelson, Levada, Alexandre, Deledalle, Charles-Alban, Dahdouh, Sonia, Image, Modélisation, Analyse, GEométrie, Synthèse (IMAGES), Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris-Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Département Traitement du Signal et des Images (TSI), Télécom ParisTech-Centre National de la Recherche Scientifique (CNRS), and HAL, TelecomParis
- Subjects
[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV] ,image denoising ,Computer Science::Computer Vision and Pattern Recognition ,[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] ,non local means ,ultrasound image ,multiple noises ,ultrasound segmentation - Abstract
International audience; In this paper, an extension of the framework proposed by Deledalleet al.[1] for Non Local Means (NLM) method is proposed. This extensionis a general adaptive method to denoise images containingmultiple noises. It takes into account a segmentation stage that indicatesthe noise type of a given pixel in order to select the similaritymeasure and suitable parameters to perform the denoising task, consideringa certain patch on the image. For instance, it has been experimentallyobserved that fetal 3D ultrasound images are corruptedby different types of noise, depending on the tissue. Finally, the proposedmethod is applied to denoise these images, showing very goodresults.
- Published
- 2014
26. Accelerating GMM-Based Patch Priors for Image Restoration: Three Ingredients for a $100{\times}$ Speed-Up.
- Author
-
Parameswaran, Shibin, Deledalle, Charles-Alban, Denis, Loic, and Nguyen, Truong Q.
- Subjects
- *
IMAGE reconstruction , *IMAGE processing , *GAUSSIAN mixture models , *IMAGE quality analysis , *SIGNAL denoising - Abstract
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 the 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, the FEPLL is the first algorithm that can competitively restore a $512\times 512$ pixel image in under 0.5 s for all the degradations mentioned earlier without specialized code optimizations, such as CPU parallelization or GPU implementation. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
27. Image Denoising with Generalized Gaussian Mixture Model Patch Priors.
- Author
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Deledalle, Charles-Alban, Parameswaran, Shibin, and Nguyen, Truong Q.
- Subjects
GAUSSIAN mixture models ,IMAGE denoising ,IMAGE reconstruction ,IMAGE processing ,ALPHA rhythm - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
28. Débruitage d'images au-delà du bruit additif gaussien - Estimateurs à patchs et leur application à l'imagerie SAR
- Author
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Deledalle, Charles-Alban, Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Telecom ParisTech, Florence Tupin(Florence.tupin@telecom-paristech.fr), Deledalle, Charles-Alban, and Télécom ParisTech
- Subjects
non-local means ,[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing ,modèles à patchs ,bruits non-gaussiens ,patch-based models ,bruit non-gaussien ,[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing ,non-gaussian noise ,SAR imagery ,imagerie SAR ,non-Gaussian noises ,moyennes non-locales ,Image denoising ,patch models ,Débruitage d'image ,[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing ,Débruitage d'images ,[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing - Abstract
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., 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.
- Published
- 2011
29. Change Classification of Multi-Temporal SAR Series by Generalized Likelihood Ratio Test
- Author
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Su, Xin, Deledalle, Charles-Alban, Tupin, Florence, Sun, Hong, Image, Modélisation, Analyse, GEométrie, Synthèse (IMAGES), Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris-Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Département Traitement du Signal et des Images (TSI), Télécom ParisTech-Centre National de la Recherche Scientifique (CNRS), and HAL, TelecomParis
- Subjects
[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV] ,[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] ,ComputingMilieux_MISCELLANEOUS - Abstract
International audience
- Published
- 2014
30. NORCAMA: Change Analysis in SAR Time Series by Likelihood Ratio Change Matrix Clustering
- Author
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SU, Xin, DELEDALLE, Charles-Alban, TUPIN, Florence, SUN, Hong, Département Traitement du Signal et des Images (TSI), Télécom ParisTech-Centre National de la Recherche Scientifique (CNRS), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), School of Electronic Information [Wuhan], and Wuhan University [China]
- Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing ,Computer Science::Computer Vision and Pattern Recognition ,[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing - Abstract
This paper presents a likelihood ratio test based method of change detection and classification for synthetic aperture radar (SAR) time series, namely NORmalized Cut on chAnge criterion MAtrix (NORCAMA). This method involves three steps: 1) multi-temporal pre-denoising step over the whole image series to reduce the effect of the speckle noise; 2) likelihood ratio test based change criteria between two images using both the original noisy images and the denoised images; 3) change classification by a normalized cut based clustering-and-recognizing method on change criterion matrix (CCM). The experiments on both synthetic and real SAR image series show the effective performance of the proposed framework.
- Published
- 2014
31. Additional illustrations of NL-SAR method for resolution-preserving (Pol)(In)SAR denoising
- Author
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DELEDALLE, Charles-Alban, DENIS, Loïc, TUPIN, Florence, REIGBER, Andreas, JÄGER, Marc, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Hubert Curien [Saint Etienne] (LHC), Université Jean Monnet [Saint-Étienne] (UJM)-Centre National de la Recherche Scientifique (CNRS)-Institut d'Optique Graduate School (IOGS), Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Télécom ParisTech, DLR Microwaves and Radar Institute, German Aerospace Center (DLR), plafrim, labex PRIMES (ANR-11-LABX-0063), and CPU
- Subjects
Synthetic Aperture Radar (SAR) ,Interferometry ,Non-local means ,[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] ,Polarimetry ,Estimation - Abstract
This document provides additional information and results of the method NL-SAR described in our paper: "NL-SAR: a unified Non-Local framework for resolution-preserving (Pol)(In)SAR denoising" submitted to IEEE Trans. on Geoscience and Remote Sensing [Deledalle et al., 2013]. NL-SAR is a fully automatic method for speckle reduction that handles amplitude, polarimetric and/or interferometric SAR data. It can process single look and multi-look images. The source code of the method is freely available at: http://www.math.u-bordeaux1.fr/cdeledal/nlsar.php.
- Published
- 2014
32. TV-NL : Une coopération entre les NL-means et les méthodes variationnelles
- Author
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SUTOUR, Camille, AUJOL, Jean-François, DELEDALLE, Charles-Alban, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), and Sutour, Camille
- Subjects
[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV] ,[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] - Abstract
International audience; Cet article traite du problème de débruitage des images via une nouvelle formulation variationnelle exploitant localement le caractère redondant ou bien régulier des images. Nous effectuons une minimisation de la variation totale avec une attache aux données non locale. En tirant parti à la fois des approches non locales et variationnelles, nous montrons que l'on peut pallier deux défauts respectifs de ces deux approches : l'effet de crénelage et de bruit résiduel. Par ailleurs, le modèle proposé s'étend à différentes statistiques de bruit et nous montrons qu'une solution peut être obtenue efficacement dans le cas de lois issues de la famille exponentielle. Les résultats obtenus, en associant l'approche non locale aux méthodes variationnelles, montrent une réduction des effets de crénelage et de bruit résiduel. Nos résultats offrent à la fois un gain un terme de qualité visuelle et de rapport signal à bruit.
- Published
- 2013
33. Stein COnsistent Risk Estimator (SCORE) for hard thresholding
- Author
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Deledalle, Charles-Alban, Peyr��, Gabriel, Fadili, Jalal, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), CEntre de REcherches en MAthématiques de la DEcision (CEREMADE), Centre National de la Recherche Scientifique (CNRS)-Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Equipe Image - Laboratoire GREYC - UMR6072, Groupe de Recherche en Informatique, Image et Instrumentation de Caen (GREYC), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU), and CPU
- Subjects
FOS: Computer and information sciences ,[STAT.AP]Statistics [stat]/Applications [stat.AP] ,hard thresholding ,sparsity ,Mathematics - Statistics Theory ,Statistics Theory (math.ST) ,[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH] ,Statistics - Applications ,[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing ,[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] ,[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] ,FOS: Mathematics ,risk estimation ,Applications (stat.AP) ,threshold selection ,[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing - Abstract
International audience; 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.
- Published
- 2013
34. Stable Recovery with Analysis Decomposable Priors
- Author
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Fadili, Jalal M., Peyré, Gabriel, Vaiter, Samuel, Deledalle, Charles-Alban, Salmon, Joseph, Queau, Yvain, Equipe Image - Laboratoire GREYC - UMR6072, Groupe de Recherche en Informatique, Image et Instrumentation de Caen (GREYC), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU), CEntre de REcherches en MAthématiques de la DEcision (CEREMADE), Centre National de la Recherche Scientifique (CNRS)-Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Traitement et Communication de l'Information (LTCI), and Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
Mathematics - Functional Analysis ,FOS: Computer and information sciences ,[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV] ,Optimization and Control (math.OC) ,Computer Science - Information Theory ,[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] ,Information Theory (cs.IT) ,FOS: Mathematics ,Mathematics - Optimization and Control ,Functional Analysis (math.FA) - Abstract
In this paper, we investigate in a unified way the structural properties of solutions to inverse problems. These solutions are regularized by the generic class of semi-norms defined as a decomposable norm composed with a linear operator, the so-called analysis type decomposable prior. This encompasses several well-known analysis-type regularizations such as the discrete total variation (in any dimension), analysis group-Lasso or the nuclear norm. Our main results establish sufficient conditions under which uniqueness and stability to a bounded noise of the regularized solution are guaranteed. Along the way, we also provide a strong sufficient uniqueness result that is of independent interest and goes beyond the case of decomposable norms., Comment: 4 pages, to appear in SAMPTA 2013
- Published
- 2013
35. PARISAR: Patch-Based Estimation and Regularized Inversion for Multibaseline SAR Interferometry.
- Author
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Ferraioli, Giampaolo, Deledalle, Charles-Alban, Denis, Loic, and Tupin, Florence
- Subjects
- *
ANTENNAS (Electronics) , *SYNTHETIC aperture radar , *IMAGING systems , *INTERFEROMETRY , *DIFFRACTION patterns , *REGULARIZATION parameter - Abstract
Reconstruction of elevation maps from a collection of synthetic aperture radar (SAR) images obtained in interferometric configuration is a challenging task. Reconstruction methods must overcome two difficulties: the strong interferometric noise that contaminates the data and the 2 phase ambiguities. Interferometric noise requires some form of smoothing among pixels of identical height. Phase ambiguities can be solved, up to a point, by combining linkage to the neighbors and a global optimization strategy to prevent from being trapped in local minima. This paper introduces a reconstruction method, PARISAR, that achieves both a resolution-preserving denoising and a robust phase unwrapping (PhU) by combining nonlocal denoising methods based on patch similarities and total-variation regularization. The optimization algorithm, based on graph cuts, identifies the global optimum. Combining patch-based speckle reduction methods and regularization-based PhU requires solving several issues: 1) computational complexity, the inclusion of nonlocal neighborhoods strongly increasing the number of terms involved during the regularization, and 2) adaptation to varying neighborhoods, patch comparison leading to large neighborhoods in homogeneous regions and much sparser neighborhoods in some geometrical structures. PARISAR solves both issues. We compare PARISAR with other reconstruction methods both on numerical simulations and satellite images and show a qualitative and quantitative improvement over state-of-the-art reconstruction methods for multibaseline SAR interferometry. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
36. Risk estimation for matrix recovery with spectral regularization
- Author
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Deledalle, Charles-Alban, Vaiter, Samuel, Peyré, Gabriel, Fadili, Jalal M., Dossal, Charles, CEntre de REcherches en MAthématiques de la DEcision (CEREMADE), Centre National de la Recherche Scientifique (CNRS)-Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Equipe Image - Laboratoire GREYC - UMR6072, Groupe de Recherche en Informatique, Image et Instrumentation de Caen (GREYC), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU), Institut de Mathématiques de Bordeaux (IMB), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
FOS: Computer and information sciences ,Computer Science - Information Theory ,proximal algorithms ,Machine Learning (stat.ML) ,Mathematics - Statistics Theory ,Statistics Theory (math.ST) ,Machine Learning (cs.LG) ,[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] ,[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing ,[STAT.ML]Statistics [stat]/Machine Learning [stat.ML] ,[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] ,Statistics - Machine Learning ,FOS: Mathematics ,Mathematics - Optimization and Control ,Information Theory (cs.IT) ,SURE ,[MATH.MATH-IT]Mathematics [math]/Information Theory [math.IT] ,[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH] ,matrix recovery ,Risk estimation ,spectral regularization ,Computer Science - Learning ,[INFO.INFO-IT]Computer Science [cs]/Information Theory [cs.IT] ,Optimization and Control (math.OC) ,matrix-valued function ,nuclear norm ,matrix completion ,[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing - Abstract
In this paper, we develop an approach to recursively estimate the quadratic risk for matrix recovery problems regularized with spectral functions. Toward this end, in the spirit of the SURE theory, a key step is to compute the (weak) derivative and divergence of a solution with respect to the observations. As such a solution is not available in closed form, but rather through a proximal splitting algorithm, we propose to recursively compute the divergence from the sequence of iterates. A second challenge that we unlocked is the computation of the (weak) derivative of the proximity operator of a spectral function. To show the potential applicability of our approach, we exemplify it on a matrix completion problem to objectively and automatically select the regularization parameter., This version is an update of our original paper presented at ICML'2012 workshop on Sparsity, Dictionaries and Projections in Machine Learning and Signal Processing
- Published
- 2012
37. How to combine TerraSAR-X and CosmoSkyMed Images for a better scene understanding ?
- Author
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Sportouche, H., Deledalle, Charles-Alban, Tupin, Florence, Nicolas, J. M., Perciano, Talita, HAL, TelecomParis, Image, Modélisation, Analyse, GEométrie, Synthèse (IMAGES), Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris-Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Département Traitement du Signal et des Images (TSI), and Télécom ParisTech-Centre National de la Recherche Scientifique (CNRS)
- Subjects
[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV] ,[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] ,ComputingMilieux_MISCELLANEOUS - Abstract
International audience
- Published
- 2012
38. Automatic estimation of the noise level function for adaptive blind denoising.
- Author
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Sutour, Camille, Aujol, Jean-Francois, and Deledalle, Charles-Alban
- Published
- 2016
- Full Text
- View/download PDF
39. Sparse-Based Morphometry: Principle and Application to Alzheimer's Disease.
- Author
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Coupé, Pierrick, Deledalle, Charles-Alban, Dossal, Charles, and Allard, Michèle
- Published
- 2016
- Full Text
- View/download PDF
40. Texture Reconstruction Guided by a High-Resolution Patch.
- Author
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El Gheche, Mireille, Aujol, Jean-Francois, Berthoumieu, Yannick, and Deledalle, Charles-Alban
- Subjects
HIGH resolution imaging ,TEXTURE analysis (Image processing) ,IMAGE reconstruction ,MATHEMATICAL regularization ,HISTOGRAMS - Abstract
In this paper, we aim at super-resolving a low-resolution texture under the assumption that a high-resolution patch of the texture is available. To do so, we propose a variational method that combines two approaches that are texture synthesis and image reconstruction. The resulting objective function holds a nonconvex energy that involves a quadratic distance to the low-resolution image, a histogram-based distance to the high-resolution patch, and a nonlocal regularization that links the missing pixels with the patch pixels. As for the histogram-based measure, we use a sum of Wasserstein distances between the histograms of some linear transformations of the textures. The resulting optimization problem is efficiently solved with a primal-dual proximal method. Experiments show that our method leads to a significant improvement, both visually and numerically, with respect to the state-of-the-art algorithms for solving similar problems. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
41. On Debiasing Restoration Algorithms: Applications to Total-Variation and Nonlocal-Means.
- Author
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Deledalle, Charles-Alban, Papadakis, Nicolas, and Salmon, Joseph
- Published
- 2015
- Full Text
- View/download PDF
42. Combining patch-based estimation and total variation regularization for 3D InSAR reconstruction.
- Author
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Deledalle, Charles-Alban, Denis, Loic, Ferraioli, Giampaolo, and Tupin, Florence
- Published
- 2015
- Full Text
- View/download PDF
43. Patch-based SAR image classification: The potential of modeling the statistical distribution of patches with Gaussian mixtures.
- Author
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Tabti, Sonia, Deledalle, Charles-Alban, Denis, Loic, and Tupin, Florence
- Published
- 2015
- Full Text
- View/download PDF
44. Image Zoom Completion.
- Author
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Hidane, Moncef, El Gheche, Mireille, Aujol, Jean-Francois, Berthoumieu, Yannick, and Deledalle, Charles-Alban
- Subjects
PROGRAM transformation ,IMAGE reconstruction ,DIGITAL images ,SIGNAL convolution ,HIGH resolution imaging - Abstract
We consider the problem of recovering a high-resolution image from a pair consisting of a complete low-resolution image and a high-resolution but incomplete one. We refer to this task as the image zoom completion problem. After discussing possible contexts in which this setting may arise, we introduce a nonlocal regularization strategy, giving full details concerning the numerical optimization of the corresponding energy and discussing its benefits and shortcomings. We also derive two total variation-based algorithms and evaluate the performance of the proposed methods on a set of natural and textured images. We compare the results and get with those obtained with two recent state-of-the-art single-image super-resolution algorithms. [ABSTRACT FROM PUBLISHER]
- Published
- 2016
- Full Text
- View/download PDF
45. Estimation of the Noise Level Function Based on a Nonparametric Detection of Homogeneous Image Regions.
- Author
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Sutour, Camille, Deledalle, Charles-Alban, and Aujol, Jean-François
- Subjects
ACOUSTIC transients ,APPROXIMATION theory ,NONPARAMETRIC estimation ,NOISE ,LOUDNESS - Abstract
We propose a two-step algorithm that 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 nonparametric test is applied to decide whether each region is homogeneous or not. Based on Kendall's τ coefficient (a rank-based measure of correlation), this detector has a nondetection rate independent of the unknown distribution of the noise, provided that it is at least spatially uncorrelated. Moreover, we prove, on a toy example, that its overall detection error vanishes with respect to the region size as soon as the signal to noise ratio level is nonzero. Once homogeneous regions are detected, the noise level function is estimated as a second order polynomial minimizing the l
1 error on the statistics of these regions. Numerical experiments show the efficiency of the proposed approach in estimating the noise level function, with a relative error under 10% obtained on a large data set. We illustrate the interest of the approach for an image denoising application. [ABSTRACT FROM AUTHOR]- Published
- 2015
- Full Text
- View/download PDF
46. Building invariance properties for dictionaries of SAR image patches.
- Author
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Tabti, Sonia, Deledalle, Charles-Alban, Denis, Loic, and Tupin, Florence
- Published
- 2014
- Full Text
- View/download PDF
47. Change detection and classification of multi-temporal SAR series based on generalized likelihood ratio comparing-and-recognizing.
- Author
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Su, Xin, Deledalle, Charles-Alban, Tupin, Florence, and Sun, Hong
- Published
- 2014
- Full Text
- View/download PDF
48. Adaptive regularization of the NL-means for video denoising.
- Author
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Sutour, Camille, Aujol, Jean-Francois, Deledalle, Charles-Alban, and Domenger, Jean-Philippe
- Published
- 2014
- Full Text
- View/download PDF
49. Denoising based on non local means for ultrasound images with simultaneous multiple noise distributions.
- Author
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Salvadeo, Denis H. P., Bloch, Isabelle, Tupin, Florence, Mascarenhas, Nelson D. A., Levada, Alexandre L. M., Deledalle, Charles-Alban, and Dahdouh, Sonia
- Published
- 2014
- Full Text
- View/download PDF
50. Modeling the distribution of patches with shift-invariance: Application to SAR image restoration.
- Author
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Tabti, Sonia, Deledalle, Charles-Alban, Denis, Loic, and Tupin, Florence
- Published
- 2014
- Full Text
- View/download PDF
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