Michaël Unser

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Publications associées (516)

SELF-SUPERVISED ISOTROPIC SUPERRESOLUTION FETAL BRAIN MRI

Michaël Unser, Meritxell Bach Cuadra

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, Shayan Aziznejad, Joaquim Gonçalves Garcia Barreto Campos

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, Shayan Aziznejad, Joaquim Gonçalves Garcia Barreto Campos

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

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