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Isotropic Inverse-Problem Approach for Two-Dimensional Phase Unwrapping

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Publications associées (29)

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Stain-free identification of cell nuclei using tomographic phase microscopy in flow cytometry

Demetri Psaltis, Joowon Lim

The accurate identification of the three-dimensional quantitative shape of a cell nucleus is now possible without fluorescent staining by applying computational segmentation to refractive index tomograms recorded in the flow cytometry mode. ...

Nature Publishing Group2022

Nonlinear Inverse Problems in Quantitative Phase Imaging

Thanh-An Michel Pham

The topic of this thesis is the development of new algorithmic reconstruction methods for quantitative phase imaging (QPI). In the past decade, advanced QPI has emerged as a valuable tool to study label-free biological samples and uncover their 3D structur ...

EPFL2022

Geodesic Distance on Optimally Regularized Functional Connectomes Uncovers Individual Fingerprints

Enrico Amico

Background: Functional connectomes (FCs) have been shown to provide a reproducible individual fingerprint, which has opened the possibility of personalized medicine for neuro/psychiatric disorders. Thus, developing accurate ways to compare FCs is essential ...

MARY ANN LIEBERT, INC2021

Deep-learning projector for optical diffraction tomography

Michaël Unser, Thanh-An Michel Pham, Harshit Gupta

Optical diffraction tomography is an effective tool to estimate the refractive indices of unknown objects. It proceeds by solving an ill-posed inverse problem for which the wave equation governs the scattering events. The solution has traditionally been de ...

2020

Graph Laplacian Mixture Model

Pascal Frossard, Hermina Petric Maretic

Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all avai ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

A Unifying Representer Theorem for Inverse Problems and Machine Learning

Michaël Unser

Regularization addresses the ill-posedness of the training problem in machine learning or the reconstruction of a signal from a limited number of measurements. The method is applicable whenever the problem is formulated as an optimization task. The standar ...

2021

Optical phase contrast imaging of human retinal cells by changing the tissue refractive index

Christophe Moser, Timothé Laforest, Laura Emmanuelle Kowalczuk, Mathieu Künzi

Purpose : Based on oblique partially coherent illumination of transparent samples, we developed a simple custom Optical Phase Imaging (OPI) microscope providing a label-free, semi-quantitative phase contrast imaging. The aim of this study was to explore th ...

ASSOC RESEARCH VISION OPHTHALMOLOGY INC2020

A Novel Spatial-Angular Domain Regularisation Approach for Restoration of Diffusion MRI

Jean-Philippe Thiran, Meritxell Bach Cuadra, Alessandro Daducci

In this paper we tackle the problem of regularisation for inverse problems in single shell diffusion weighted image restoration. Our aim is to recover a high-resolution and denoised DWI signal, prior to any model fitting. The main contribution of our metho ...

SPRINGER-VERLAG BERLIN2019

High-Contrast Imaging of Nanodiamonds in Cells by Energy Filtered and Correlative Light-Electron Microscopy: Toward a Quantitative Nanoparticle-Cell Analysis

Theo Lasser

Fluorescent nanodiamonds (fNDs) represent an emerging class of nanomaterials offering great opportunities for ultrahigh resolution imaging, sensing and drug delivery applications. Their biocompatibility, exceptional chemical and consistent photostability r ...

2019

Solving Continuous-Domain Problems Exactly with Multiresolution B-Splines

Michaël Unser, Julien René Fageot, Thomas Jean Debarre, Harshit Gupta

We propose a discretization method for continuous-domain linear inverse problems with multiple-order total-variation (TV) regularization. It is based on a recent result that proves that such inverse problems have sparse polynomial-spline solutions. Our met ...

IEEE2019

Time- and Space-Efficient Spatial Data Analytics

Mirjana Pavlovic

Advances in data acquisition technologies and supercomputing for large-scale simulations have led to an exponential growth in the volume of spatial data. This growth is accompanied by an increase in data complexity, such as spatial density, but also by mor ...

EPFL2019

3D Reconstruction of Optical Diffraction Tomography Based on a Neural Network Model

Morteza Hasani Shoreh

Optical tomography has been widely investigated for biomedical imaging applications. In recent years, it has been combined with digital holography and has been employed to produce high quality images of phase objects such as cells. In this Thesis, we look ...

EPFL2018

Efficient Inversion of Multiple-Scattering Model for Optical Diffraction Tomography

Michaël Unser, Emmanuel Emilien Louis Soubies, Thanh-An Michel Pham

Optical diffraction tomography relies on solving an inverse scattering problem governed by the wave equation. Classical reconstruction algorithms are based on linear approximations of the forward model (Born or Rytov), which limits their applicability to t ...

2017

Imaging samples larger than the field of view: the SLS experience

Goran Lovric, Alessandra Patera

Volumetric datasets with micrometer spatial and sub-second temporal resolutions are nowadays routinely acquired using synchrotron X-ray tomographic microscopy (SRXTM). Although SRXTM technology allows the examination of multiple samples with short scan tim ...

Iop Publishing Ltd2017

Q&A: Why use synchrotron x-ray tomography for multi-scale connectome mapping?

Giorgio Margaritondo

To understand how information flows and is used in the human brain, we must map neural structures at all levels, providing visualizations similar to those of Google Earth for continents, countries, cities, and streets. Unfortunately, the imaging and proces ...

2017

Splines Are Universal Solutions of Linear Inverse Problems with Generalized TV Regularization

Michaël Unser, Julien René Fageot, John Paul Ward

Splines come in a variety of flavors that can be characterized in terms of some differential operator L. The simplest piecewise-constant model corresponds to the derivative operator. Likewise, one can extend the traditional notion of total variation by con ...

2017

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Emrah Bostan

We propose new regularization models to solve inverse problems encountered in biomedical imaging applications. In formulating mathematical schemes, we base our approach on the sparse signal processing principles that have emerged as a central paradigm in t ...

EPFL2016

Joint absorption and phase retrieval in grating-based x-ray radiography

Michaël Unser, Emrah Bostan, Masih Nilchian

Given the raw absorption and differential phase-contrast images obtained from a grating-based x-ray radiography, we formulate the joint denoising of the absorption image and retrieval of the non-differential phase image as a regularized inverse problem. Th ...

Optical Society of America2016

Optical Tomographic Image Reconstruction Based on Beam Propagation and Sparse Regularization

Demetri Psaltis, Michaël Unser, Ulugbek Kamilov, Cédric René Jean Vonesch

Optical tomographic imaging requires an accurate forward model as well as regularization to mitigate missing-data artifacts and to suppress noise. Nonlinear forward models can provide more accurate interpretation of the measured data than their linear coun ...

2016

Fast 3D reconstruction method for differential phase contrast X-ray CT

Michaël Unser, Michael Thompson McCann, Masih Nilchian

We present a fast algorithm for fully 3D regularized X-ray tomography reconstruction for both traditional and differential phase contrast measurements. In many applications, it is critical to reduce the X-ray dose while producing high-quality reconstructio ...

2016