Publications de Michaël Unser

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Ill-posed linear inverse problems are frequently encountered in image reconstruction tasks. Image reconstruction methods that combine the Plug-and-Play (PnP) priors framework with convolutional neural network (CNN) based denoisers have shown impressive per ...

We present an analytical form of ground-truth k-space data for the 2-D Shepp-Logan brain phantom in the presence of multiple and non-homogeneous receiving coils. The analytical form allows us to conduct realistic simulations and validations of reconstructi ...

Cycle spinning is a widely used approach for improving the performance of wavelet-based methods that solve linear inverse problems. Extensive numerical experiments have shown that it significantly improves the quality of the recovered signal without increa ...

DeepImageJ offers a user-friendly solution in ImageJ to run trained deep learning models for biomedical image analysis. It includes guiding tools for reliable analyses, contributing to the democratization of deep learning in microscopy. ...

This paper presents two contributions. We first introduce a continuous-domain version of Principal-Component Analysis (PCA) for designing steerable filters so that they best approximate a given set of image templates. We exploit the fact that steerability ...

We introduce a new model of parametric contours defined in a continuous fashion. Our curve model relies on Hermite spline interpolation and can easily generate curves with sharp discontinuities; it also grants direct access to the tangent at each location. ...

We build wavelet-like functions based on a parametrized family of pseudo-differential operators

L _{ v }

that satisfy some admissibility and scalability conditions. The shifts of the generalized B-splines, which are localized versions of the Green func ...

A traditional photonic-force microscope (PFM) results in huge sets of data, which requires tedious numerical analysis. In this paper, we propose instead an analog signal processor to attain real-time capabilities while retaining the richness of the traditi ...

We propose a recursive data-driven risk-estimation method for non-linear iterative deconvolution. Our two main contributions are 1) a solution-domain risk-estimation approach that is applicable to non-linear restoration algorithms for ill-conditioned inver ...

We present a new family of two-dimensional and three-dimensional orthogonal wavelets which uses quincunx sampling. The orthogonal refinement filters have a simple analytical expression in the Fourier domain as a function of the order λ, which may be nonint ...

We present a new formulation of a shape space containing all continuously defined 2D spline curves up to a similarity transform of a reference shape. We are able to measure a distance between an arbitrary curve and the shape space itself. Our contribution ...

Michaël Unser

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Learning Lipschitz-Controlled Activation Functions in Neural Networks for Plug-and-Play Image Reconstruction Methods

A Nonlinear Technique for Automatic Twin-Image and Zero-Order Term Suppression in Digital Holographic Microscopy

Sub-Resolution Axial Localization of Nanoparticles in Fluorescence Microscopy

Analytical Form of Shepp-Logan Phantom for Parallel MRI

Variational Justification of Cycle Spinning for Wavelet-Based Solutions of Inverse Problems

DeepImageJ: A user-friendly environment to run deep learning models in ImageJ

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

Design Of Steerable Filters For The Detection Of Micro-Particles

Hermite Snakes with Control of Tangents

Generalized L-Spline Wavelet Bases

Reconstruction of Dynamic PET Data Using Spatio-Temporal Wavelet $ ℓ _{ 1 } $ Regularization

Intracellular nanomanipulation by a photonic-force microscope with real-time acquisition of a 3D stiffness matrix

Elastic Image Registration with Applications to Proteomics

Matched Wavelet-Like Bases for the Decoupling of Sparse Stochastic Processes

Recursive Risk Estimation For Non-Linear Image Deconvolution With A Wavelet-Domain Sparsity Constraint

An Orthogonal Family of Quincunx Wavelets with Continuously Adjustable Order

Similarity-Based Shape Priors for 2D Spline Snakes

A method for assessing the fidelity of optical diffraction tomography reconstruction methods using structured illumination

User-Friendly Building of Reconstruction Algorithms with GlobalBioIm

User-Friendly Building of Reconstruction Algorithms for Solving Inverse Problems