574 citations · 813 across the 18 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…
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…