10 citations · 20 across the 8 of their papers we have counts for
11 papers
Scalable photoacoustic tomography implementations accounting for the spatial impulse response of transducers
Trung-Thai Do, Paul Escande, Caroline Chaux +2
Iterative model-based reconstruction in photoacoustic tomography repeatedly applies the forward operator mapping the initial pressure to the transducer signals, and its adjoint. At…
DeepInverse: A Python package for solving imaging inverse problems with deep learning
Julián Tachella, Matthieu Terris, Samuel Hurault +24
DeepInverse is an open-source PyTorch-based library for solving imaging inverse problems. The library covers all crucial steps in image reconstruction from the efficient implementa…
On the Concentration of the Minimizers of Empirical Risks
Paul Escande
Obtaining guarantees on the convergence of the minimizers of empirical risks to the ones of the true risk is a fundamental matter in statistical learning. Instead of deriving guara…
Random walk informed community detection reveals heterogeneities in the lymph node conduits network
Solène Song, Malek Senoussi, Paul Escande +1
Random walks on networks are widely used to model stochastic processes such as search strategies, transportation problems or disease propagation. A prominent example of such proces…
Fast Wavelet Decomposition of Linear Operators through Product-Convolution Expansions
Paul Escande, Pierre Weiss
Wavelet decompositions of integral operators have proven their efficiency in reducing computing times for many problems, ranging from the simulation of waves or fluids to the resol…
A scalable estimator of sets of integral operators
Valentin Debarnot, Paul Escande, Pierre Weiss
We propose a scalable method to find a subspace of low-rank tensors that simultaneously approximates a set of integral operators. The method can be seen as…
Estimation of linear operators from scattered impulse responses
Jérémie Bigot, Paul Escande, Pierre Weiss
We provide a new estimator of integral operators with smooth kernels, obtained from a set of scattered and noisy impulse responses. The proposed approach relies on the formalism of…
Approximation of integral operators using convolution-product expansions
Paul Escande, Pierre Weiss
We consider a class of linear integral operators with impulse responses varying regularly in time or space. These operators appear in a large number of applications ranging from si…
Real-time -- deblurring using wavelet expansions of operators
Paul Escande, Pierre Weiss
Image deblurring is a fundamental problem in imaging, usually solved with com-putationally intensive optimization procedures. We show that the minimization can be significantly acc…
Sparse Wavelet Representations of Spatially Varying Blurring Operators
Paul Escande, Pierre Weiss
Restoring images degraded by spatially varying blur is a problem encountered in many disciplines such as astrophysics, computer vision or biomedical imaging. One of the main challe…
Image restoration using sparse approximations of spatially varying blur operators in the wavelet domain
Paul Escande, Pierre Weiss, Francois Malgouyres
Restoration of images degraded by spatially varying blurs is an issue of increasing importance in the context of photography, satellite or microscopy imaging. One of the main diffi…