12 results on '"Deledalle, Charles-Alban"'
Search Results
2. Poisson Noise Reduction with Non-local PCA
- Author
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Salmon, Joseph, Harmany, Zachary, Deledalle, Charles-Alban, and Willett, Rebecca
- Published
- 2014
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3. Non-local Methods with Shape-Adaptive Patches (NLM-SAP)
- Author
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Deledalle, Charles-Alban, Duval, Vincent, and Salmon, Joseph
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- 2012
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4. 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
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5. 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
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[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.
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- 2014
6. Image Denoising with Generalized Gaussian Mixture Model Patch Priors.
- Author
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Deledalle, Charles-Alban, Parameswaran, Shibin, and Nguyen, Truong Q.
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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]
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- 2018
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7. 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
8. NL-SAR: A Unified Nonlocal Framework for Resolution-Preserving (Pol)(In)SAR Denoising.
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Deledalle, Charles-Alban, Denis, Loïc, Tupin, Florence, Reigber, Andreas, and Jäger, Marc
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IMAGING systems , *SYNTHETIC aperture radar , *INTERFEROMETRY , *POLARIMETRY , *REMOTE sensing - Abstract
Speckle noise is an inherent problem in coherent imaging systems such as synthetic aperture radar. It creates strong intensity fluctuations and hampers the analysis of images and the estimation of local radiometric, polarimetric, or interferometric properties. Synthetic aperture radar (SAR) processing chains thus often include a multilooking (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 adapt locally the estimation. Nonlocal (NL)-means successfully adapt smoothing by deriving data-driven weights from the similarity between small image patches. The generalization of nonlocal approaches offers a flexible framework for resolution-preserving speckle reduction. We describe a general method, i.e., NL-SAR, that builds extended nonlocal neighborhoods for denoising amplitude, polarimetric, and/or interferometric SAR images. These neighborhoods are defined on the basis of pixel similarity as evaluated by multichannel comparison of patches. Several nonlocal 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 multilook 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, airborne, and spaceborne radar images. The source code of a parallel implementation of NL-SAR is released with this paper. [ABSTRACT FROM PUBLISHER]
- Published
- 2015
- Full Text
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9. 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, and Sun, Hong
- Subjects
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SYNTHETIC aperture radar , *TIME series analysis , *LIKELIHOOD ratio tests , *CLUSTERING of particles , *IMAGE denoising , *SPECKLE interference - 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. [ABSTRACT FROM AUTHOR]
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- 2015
- Full Text
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10. Two-Step Multitemporal Nonlocal Means for Synthetic Aperture Radar Images.
- Author
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Xin Su, Deledalle, Charles-Alban, Tupin, Florence, and Hong Sun
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SYNTHETIC aperture radar , *IMAGING systems , *ESTIMATION theory , *INFORMATION storage & retrieval systems , *REDUNDANCY in engineering - Abstract
This paper presents a denoising approach for multitemporal synthetic aperture radar (SAR) images based on the concept of nonlocal means (NLM). It exploits the information redundancy existing in multitemporal images by a two-step strategy. The first step realizes a nonlocal weighted estimation driven by the redundancy in time, whereas the second step makes use of the nonlocal estimation in space. Using patch similarity miss-registration estimation, we also adapted this approach to the case of unregistered SAR images. The experiments illustrate the efficiency of the proposed method to denoise multitemporal images while preserving new information. [ABSTRACT FROM PUBLISHER]
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- 2014
- Full Text
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11. Two steps multi-temporal Non-Local Means for SAR images.
- Author
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Su, Xin, Deledalle, Charles-Alban, Tupin, Florence, and Sun, Hong
- Abstract
This paper presents a denoising approach for multi-temporal Synthetic aperture radar (SAR) images based on Non-Local Means (NLM) method. To exploit redundancy existing in multi-temporal images, we develop a new strategy of NLM for multi-temporal data. Instead of directly overspreading the NLM operator from one image to temporal images, a two steps weighted average is proposed in this paper. The first step is a maximum likelihood estimate with binary weights on temporal pixels and the second step is iterative NL means on spatial pixels. Experiments in this paper illustrate that the proposed method can effectively exploit image redundancy and denoise multi-temporal images. [ABSTRACT FROM PUBLISHER]
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- 2012
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12. Iterative Weighted Maximum Likelihood Denoising With Probabilistic Patch-Based Weights.
- Author
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Deledalle, Charles-Alban, Denis, Loïc, and Tupin, Florence
- Subjects
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NOISE control , *IMAGE processing , *PIXELS , *SIGNAL-to-noise ratio , *SYNTHETIC aperture radar , *RANDOM noise theory - Abstract
Image denoising is an important problem in image processing since noise may interfere with visual or automatic interpretation. This paper presents a new approach for image denoising in the case of a known uncorrelated noise model. The proposed filter is an extension of the nonlocal means (NL means) algorithm introduced by Buades et al, which performs a weighted average of the values of similar pixels. Pixel similarity is defined in NL means as the Euclidean distance between patches (rectangular windows centered on each two pixels). In this paper, a more general and statistically grounded similarity criterion is proposed which depends on the noise distribution model. The denoising process is expressed as a weighted maximum likelihood estimation problem where the weights are derived in a data-driven way. These weights can be iteratively refined based on both the similarity between noisy patches and the similarity of patches extracted from the previous estimate. We show that this iterative process noticeably improves the denoising performance, especially in the case of low signal-to-noise ratio images such as synthetic aperture radar (SAR) images. Numerical experiments illustrate that the technique can be successfully applied to the classical case of additive Gaussian noise but also to cases such as multiplicative speckle noise. The proposed denoising technique seems to improve on the state of the art performance in that latter case. [ABSTRACT FROM AUTHOR]
- Published
- 2009
- Full Text
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