574 citations · 813 across the 18 of their papers we have counts for
21 papers
Patch-based adaptive temporal filter and residual evaluation
Weiying Zhao, Paul Riot, Charles-Alban Deledalle +3
In coherent imaging systems, speckle is a signal-dependent noise that visually strongly degrades images' appearance. A huge amount of SAR data has been acquired from different sens…
Multitemporal SAR images change detection and visualization using RABASAR and simplified GLR
Weiying Zhao, Charles-Alban Deledalle, Loïc Denis +3
Understanding the state of changed areas requires that precise information be given about the changes. Thus, detecting different kinds of changes is important for land surface moni…
WaveQ: Gradient-Based Deep Quantization of Neural Networks through Sinusoidal Adaptive Regularization
Ahmed T. Elthakeb, Prannoy Pilligundla, Fatemehsadat Mireshghallah +3
As deep neural networks make their ways into different domains, their compute efficiency is becoming a first-order constraint. Deep quantization, which reduces the bitwidth of the…
Low-rank matrix denoising for count data using unbiased Kullback-Leibler risk estimation
Jérémie Bigot, Charles Deledalle
Many statistical studies are concerned with the analysis of observations organized in a matrix form whose elements are count data. When these observations are assumed to follow a P…
Machine learning in acoustics: theory and applications
Michael J. Bianco, Peter Gerstoft, James Traer +4
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 transforma…
Refitting solutions promoted by sparse analysis regularization with block penalties
Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon +1
In inverse problems, the use of an analysis regularizer induces a bias in the estimated solution. We propose a general refitting framework for removing this artifact wh…
Image denoising with generalized Gaussian mixture model patch priors
Charles-Alban Deledalle, Shibin Parameswaran, Truong Q. Nguyen
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 (E…
A unified framework for hard and soft clustering with regularized optimal transport
Jean-Frédéric Diebold, Nicolas Papadakis, Arnaud Dessein +1
In this paper, we formulate the problem of inferring a Finite Mixture Model from discrete data as an optimal transport problem with entropic regularization of parameter .…
Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100 speed-up
Shibin Parameswaran, Charles-Alban Deledalle, Loïc Denis +1
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…
MuLoG, or How to apply Gaussian denoisers to multi-channel SAR speckle reduction?
Charles-Alban Deledalle, Loïc Denis, Sonia Tabti +1
Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) imaging. Since most current and planned SAR imaging satellites operate in polarimetric, interferometric…
Characterizing the maximum parameter of the total-variation denoising through the pseudo-inverse of the divergence
Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon +1
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 solu…
CLEAR: Covariant LEAst-square Re-fitting with applications to image restoration
C-A. Deledalle, N. Papadakis, J. Salmon +1
In this paper, we propose a new framework to remove parts of the systematic errors affecting popular restoration algorithms, with a special focus for image processing tasks. Genera…
Generalized SURE for optimal shrinkage of singular values in low-rank matrix denoising
Jérémie Bigot, Charles Deledalle, Delphine Féral
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 famil…
Estimation of Kullback-Leibler losses for noisy recovery problems within the exponential family
Charles-Alban Deledalle
We address the question of estimating Kullback-Leibler losses rather than squared losses in recovery problems where the noise is distributed within the exponential family. Inspired…
On debiasing restoration algorithms: applications to total-variation and nonlocal-means
Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon
Bias in image restoration algorithms can hamper further analysis, typically when the intensities have a physical meaning of interest, e.g., in medical imaging. We propose to suppre…
Stein Unbiased GrAdient estimator of the Risk (SUGAR) for multiple parameter selection
Charles-Alban Deledalle, Samuel Vaiter, Jalal M. Fadili +1
Algorithms to solve variational regularization of ill-posed inverse problems usually involve operators that depend on a collection of continuous parameters. When these operators en…
Stable Recovery with Analysis Decomposable Priors
M. J. Fadili, G. Peyré, S. Vaiter +2
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 define…
Template matching with noisy patches: A contrast-invariant GLR test
Charles-Alban Deledalle, Loïc Denis, Florence Tupin
Matching patches from a noisy image to atoms in a dictionary of patches is a key ingredient to many techniques in image processing and computer vision. By representing with a singl…
Stein COnsistent Risk Estimator (SCORE) for hard thresholding
Charles-Alban Deledalle, Gabriel Peyré, Jalal Fadili
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 thresho…
Poisson noise reduction with non-local PCA
Joseph Salmon, Zachary Harmany, Charles-Alban Deledalle +1
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 conce…
Risk estimation for matrix recovery with spectral regularization
Charles-Alban Deledalle, Samuel Vaiter, Gabriel Peyré +2
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…