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Charles Deledalle

Short bio

I received the engineer degree from EPITA and the Master of Science and Technology from Univ. Paris VI both in France, in 2008. In 2011, I defended my PhD, from LTCI, Telecom ParisTech, France, in signal and image processing and supervised by Florence Tupin and Loïc Denis. I made a postdoctoral fellowship in applied mathematics at CEREMADE, Univ. Paris IX, France, in 2011-2012, under the supervision of Gabriel Peyré and Jalal Fadili. I am a CNRS researcher at IMB, Univ. Bordeaux, France, and currently a visiting professor at UC San Diego in the ECE department. My research interests include image restoration and inverse-problems with a focus on parameter estimation. I received the IEEE ICIP Best Student Paper Award in 2010, the ISIS/EEA/GRETSI Best PhD Award in 2012, the IEEE GRSS Transactions Prize Paper Award in 2016, and the UCSD ECE Best Lecturer Award in 2019.

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Recent publications

Some of the publications below have appeared in an IEEE journal, Springer journal, Elsevier journal or conference record. By allowing you to download them, I am required to post the following copyright reminder: "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."

Estimation of Kullback-Leibler losses for noisy recovery problems within the exponential family,
Charles-Alban Deledalle
Electronic Journal of Statistics, vol. 11, no. 2, pp. 3141-3164, 2017 (Project Euclid, HAL, ArXiv)
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. We exhibit conditions under which these losses can be unbiasedly estimated or estimated with a controlled bias. Simulations on parameter selection problems in image denoising applications with Gamma and Poisson noises illustrate the interest of Kullback-Leibler losses and the proposed estimators.
MuLoG, or How to apply Gaussian denoisers to multi-channel SAR speckle reduction?,
Charles-Alban Deledalle, Loïc Denis, Sonia Tabti, Florence Tupin
IEEE Transactions on Image Processing, vol. 26, no. 9, pp. 4389-4403, 2017 (IEEE Xplore, recommended pdf, HAL)
Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) imaging. Since most current and planned SAR imaging satellites operate in polarimetric, interferometric or tomographic modes, SAR images are multi-channel and speckle reduction techniques must jointly process all channels to recover polarimetric and interferometric information. The distinctive nature of SAR signal (complex-valued, corrupted by multiplicative fluctuations) calls for the development of specialized methods for speckle reduction. Image denoising is a very active topic in image processing with a wide variety of approaches and many denoising algorithms available, almost always designed for additive Gaussian noise suppression. This paper proposes a general scheme, called MuLoG (MUlti-channel LOgarithm with Gaussian denoising), to include such Gaussian denoisers within a multi-channel SAR speckle reduction technique. A new family of speckle reduction algorithms can thus be obtained, benefiting from the ongoing progress in Gaussian denoising, and offering several speckle reduction results often displaying method-specific artifacts that can be dismissed by comparison between results.
CLEAR: Covariant LEAst-square Re-fitting with applications to image restoration,
C-A. Deledalle, N. Papadakis, J. Salmon and S. Vaiter
SIAM Journal on Imaging Sciences, vol. 10, no. 1, pp. 243-284, 2017 (epubs SIAM, HAL, ArXiv)
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. Extending ideas that emerged for l1 regularization, we develop an approach that can help re-fitting the results of standard methods towards the input data. Total variation regularizations and non-local means are special cases of interest. We identify important covariant information that should be preserved by the re-fitting method, and emphasize the importance of preserving the Jacobian (w.r.t to the observed signal) of the original estimator. Then, we provide an approach that has a ``twicing'' flavor and allows re-fitting the restored signal by adding back a local affine transformation of the residual term. We illustrate the benefits of our method on numerical simulations for image restoration tasks.

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