E
Emmanuel Froustey
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 5
Citations - 2233
Emmanuel Froustey is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Iterative reconstruction & Iterative method. The author has an hindex of 5, co-authored 5 publications receiving 1391 citations.
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Journal ArticleDOI
Deep Convolutional Neural Network for Inverse Problems in Imaging
TL;DR: In this paper, the authors proposed a deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems, which combines multiresolution decomposition and residual learning in order to learn to remove these artifacts while preserving image structure.
Journal ArticleDOI
Deep Convolutional Neural Network for Inverse Problems in Imaging
TL;DR: The proposed network outperforms total variation-regularized iterative reconstruction for the more realistic phantoms and requires less than a second to reconstruct a $512\times 512$ image on the GPU.
Journal ArticleDOI
Variational Phase Imaging Using the Transport-of-Intensity Equation
TL;DR: A variational phase retrieval algorithm that combines different ranges of spatial frequencies - depending on the defocus value of the measurements - in a regularized fashion for the imaging of transparent objects is introduced.
Proceedings ArticleDOI
Phase retrieval by using transport-of-intensity equation and differential interference contrast microscopy
TL;DR: This work presents a variational reconstruction algorithm based on an iterative reconstruction algorithm involving the total variation regularisation which is efficiently solved via the alternating direction method of multipliers and demonstrates the applicability of the method via real data experiments.
Proceedings ArticleDOI
Digital phase reconstruction via iterative solutions of transport-of-intensity equation
TL;DR: In this paper, a variational algorithm for reconstructing phase objects from a series of bright field micrographs is proposed, which links the phase of a complex field to the axial derivative of its intensity.