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Michaël Unser

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Related publications (516)

SELF-SUPERVISED ISOTROPIC SUPERRESOLUTION FETAL BRAIN MRI

Superresolution T2-weighted fetal-brain magnetic-resonance imaging (FBMRI) traditionally relies on the availability of several orthogonal low-resolution series of 2-dimensional thick slices (volumes). In practice, only a few low-resolution volumes are acqu ...

IEEE2023

Measuring Complexity of Learning Schemes Using Hessian-Schatten Total Variation

Michaël Unser, Joaquim Gonçalves Garcia Barreto Campos, Shayan Aziznejad

2023

Chemical Sensors with Deep Spatiotemporal Priors

Michaël Unser

We propose a variational approach to recover concentration from raw fluorescence images of chemical sensors. This allows us to impose prior knowledge regarding the spatiotemporal distribution of the concentration while accounting for the sensor kinetics. ...

Optical Society of America2023

Learning of Continuous and Piecewise-Linear Functions With Hessian Total-Variation Regularization

Michaël Unser, Joaquim Gonçalves Garcia Barreto Campos, Shayan Aziznejad

We develop a novel 2D functional learning framework that employs a sparsity-promoting regularization based on second-order derivatives. Motivated by the nature of the regularizer, we restrict the search space to the span of piecewise-linear box splines shi ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Sparsest Univariate Learning Models Under Lipschitz Constraint

Michaël Unser, Shayan Aziznejad, Thomas Jean Debarre

Beside the minimizationof the prediction error, two of the most desirable properties of a regression scheme are stability and interpretability. Driven by these principles, we propose continuous-domain formulations for one-dimensional regression problems. I ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Bona Fide Riesz Projections For Density Estimation

Michaël Unser, Pol del Aguila Pla

The projection of sample measurements onto a reconstruction space represented by a basis on a regular grid is a powerful and simple approach to estimate a probability density function. In this paper, we focus on Riesz bases and propose a projection operato ...

IEEE2022

Asymptotic Stability in Reservoir Computing

Michaël Unser, Jonathan Yuelin Dong

Reservoir Computing is a class of Recurrent Neural Networks with internal weights fixed at random. Stability relates to the sensitivity of the network state to perturbations. It is an important property in Reservoir Computing as it directly impacts perform ...

IEEE2022

Artifacts in optical projection tomography due to refractive-index mismatch: model and correction

Michaël Unser, Jonathan Yuelin Dong

Optical projection tomography (OPT) is a powerful tool for three-dimensional (3D) imaging of mesoscopic samples. While it is able to achieve resolution of a few tens of microns over a sample volume of several cubic centimeters, the reconstructed images oft ...

2022

Steer'n'Detect: fast 2D template detection with accurate orientation estimation

Michaël Unser, Daniel Sage, Zsuzsanna Püspöki, Julien René Pierre Fageot

Motivation: Rotated template matching is an efficient and versatile algorithm to analyze microscopy images, as it automates the detection of stereotypical structures, such as organelles that can appear at any orientation. Its performance however quickly de ...

2022

Bayesian Inversion for Nonlinear Imaging Models Using Deep Generative Priors

Michaël Unser, Thanh-An Michel Pham, Pakshal Narendra Bohra, Jonathan Yuelin Dong

Most modern imaging systems incorporate a computational pipeline to infer the image of interest from acquired measurements. The Bayesian approach to solve such ill-posed inverse problems involves the characterization of the posterior distribution of the im ...

2022

Coupled Splines for Sparse Curve Fitting

Michaël Unser, Shayan Aziznejad, Thomas Jean Debarre

We formulate as an inverse problem the construction of sparse parametric continuous curve models that fit a sequence of contour points. Our prior is incorporated as a regularization term that encourages rotation invariance and sparsity. We prove that an op ...

2022

Opportunities and Challenges for Generative Adversarial Reconstruction by Distribution Matching (CryoGAN)

Michaël Unser, Pakshal Narendra Bohra

2022

Mechanical artifacts in optical projection tomography: classification and automatic calibration

Michaël Unser, Thanh-An Michel Pham, François Thierry M Marelli, Jonathan Yuelin Dong

Optical projection tomography (OPT) is a powerful tool for biomedical studies. It achieves 3D visualization of mesoscopic biological samples with high spatial resolution using conventional tomographic-reconstruction algorithms. However, various artifacts d ...

Optica Publishing Group2022

Diffraction Tomography from Single-Molecule Localization Microscopy: Numerical Feasibility

Michaël Unser, Thanh-An Michel Pham, Ferréol Arnaud Marie Soulez, Emmanuel Emilien Louis Soubies

Single-molecule localization microscopy (SMLM) is a fluorescence microscopy technique that achieves super-resolution imaging by sequentially activating and localizing random sparse subsets of fluorophores. Each activated fluorophore emits light that then s ...

IEEE2021

Optical diffraction tomography from single-molecule localization microscopy

Michaël Unser, Thanh-An Michel Pham, Ferréol Arnaud Marie Soulez, Emmanuel Emilien Louis Soubies

Single-molecule localization microscopy (SMLM) is a powerful method for the imaging of cellular structures. This modality delivers nanoscale resolution by sequentially activating a subset of fluorescent molecules and by extracting their super-resolved posi ...

2021

CryoGAN: A New Reconstruction Paradigm for Single-Particle Cryo-EM Via Deep Adversarial Learning

Michaël Unser, Laurène Donati, Harshit Gupta, Michael Thompson McCann

We present CryoGAN, a new paradigm for single-particle cryo-electron microscopy (cryo-EM) reconstruction based on unsupervised deep adversarial learning. In single-particle cryo-EM, the structure of a biomolecule needs to be reconstructed from a large set ...

2021

Principled Design and Implementation of Steerable Detectors

Michaël Unser, Adrien Raphaël Depeursinge, Zsuzsanna Püspöki, Julien René Pierre Fageot

We provide a complete pipeline for the detection of patterns of interest in an image. In our approach, the patterns are assumed to be adequately modeled by a known template, and are located at unknown positions and orientations that we aim at retrieving. W ...

2021

A constrained method for lensless coherent imaging of thin samples

Michaël Unser, Christophe Moser, Manon Rostykus

Lensless inline holography can produce high-resolution images over a large field of view (FoV). In a previous work [Appl. Opt.60, B38 (2021)], we showed that (i) the actual FoV can be extrapolated outside of the camera FoV and (ii) the effective resolution ...

2021

Convex optimization in sums of Banach spaces

Michaël Unser, Shayan Aziznejad

We characterize the solution of a broad class of convex optimization problems that address the reconstruction of a function from a finite number of linear measurements. The underlying hypothesis is that the solution is decomposable as a finite sum of compo ...

2021

Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features

Michaël Unser, Daniel Sage, Olivier Burri, Cathrin Brisken, Quentin Juppet, Fabio De Martino, Martin Weigert

Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis gener ...

2021

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