Publications by Michaël Unser

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We consider the problem of optimizing the parameters of an arbitrary denoising algorithm by minimizing Stein's Unbiased Risk Estimate (SURE) which provides a means of assessing the true mean-squared-error (MSE) purely from the measured data assuming that i ...

We present a novel framework for the reconstruction of 1D composite signals assumed to be a mixture of two additive components, one sparse and the other smooth, given a finite number of linear measurements. We formulate the reconstruction problem as a cont ...

We introduce a variational phase retrieval algorithm for the imaging of transparent objects. Our formalism is based on the transport-of-intensity equation (TIE), which relates the phase of an optical field to the variation of its intensity along the direct ...

Tracking algorithms are traditionally based on either a variational approach or a Bayesian one. In the variational case, a cost function is established between two consecutive frames and minimized by standard optimization algorithms. In the Bayesian case, ...

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 ...

Super-resolution localization microscopy methods such as PALM and STORM have been shown to provide imaging with resolutions up to a few tens of nanometers while using relatively simple setups. Biplane PALM has extended the PALM technique to three-dimension ...

FMRI time course processing is traditionally performed using linear regression followed by statistical hypothesis testing. While this analysis method is robust against noise, it relies strongly on the signal model. In this paper, we propose a non-parametric framework that is based on two main ideas. First, we introduce a problem-specific type of wavelet basis, for which we coin the term "activelets". The design of these wavelets is inspired by the form of the canonical hemodynamic response function. Second,

In a previous paper, we proposed a general Fourier method which provides an accurate prediction of the approximation error, irrespective of the scaling properties of the approximating functions. Here, we apply our results when these functions satisfy the usual two-scale relation encountered in dyadic multiresolution analysis. As a consequence of this additional constraint, the quantities introduced in our previous paper can be computed explicitly as a function of the refinement filter. This is in particular

We propose a novel denoising algorithm to reduce the Poisson noise that is typically dominant in fluorescence microscopy data. To process large datasets at a low computational cost, we use the unnormalized Haar wavelet transform. Thanks to some of its appe ...

We present an optimal spline-based algorithm for the enlargement or reduction of digital images with arbitrary (noninteger) scaling factors. This projection-based approach can be realized thanks to a new finite difference method that allows the computation of inner products with analysis functions that are B-splines of any degree n. A noteworthy property of the algorithm is that the computational complexity per pixel does not depend on the scaling factor a. For a given choice of basis functions, the results

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 ...

This paper presents an image-segmentation method which compensates multiplicative distortions based on smooth regularity assumptions. In this work, we generalize the original Chan-Vese functional to handle a continuous multiplicative bias. In the derivatio ...

We study jump-penalized estimators based on least absolute deviations which are often referred to as Potts estimators. They are estimators for a parsimonious piecewise constant representation of noisy data having a noise distribution which has heavier tail ...

We build a multiresolution analysis based on shift-invariant exponential B-spline spaces. We construct the basis functions for these spaces and for their orthogonal complements. This yields a new family of wavelet-like basis functions of

L _{ 2 }

, wit ...

In this paper, we give a general characterization of regularization functionals for vector field reconstruction, based on the requirement that the said functionals satisfy certain geometric invariance properties with respect to transformations of the coord ...

We describe an approach for exploring microscopic properties of granular media that couples x-ray microtomography and distinct-element-method (DEM) simulations through image analysis. We illustrate it via the study of the intriguing phenomenon of instant a ...

We introduce a method for multichannel restoration of images in which there is severely limited knowledge about the undegraded signal, and possibly the noise. We assume that we know the channel degradations and that there will be a significant noise reduction in a postprocessing stage in which multiple realizations are combined. This post-restoration noise reduction is often performed when working with micrographs of biological macromolecules. The restoration filters are designed to enforce a projection con

We propose a system to characterize the 3-D diffusion properties of the probing bead trapped by a photonic-force microscope. We follow a model-based approach, where the model of the dynamics of the bead is given by the Langevin equation. Our procedure comb ...

The quality of super-resolution images obtained by single-molecule localization microscopy (SMLM) depends largely on the software used to detect and accurately localize point sources. In this work, we focus on the computational aspects of super-resolution ...

The image of a sub-resolution nano-particle in fluorescence microscopy corresponds to a slice of the 3D point spread function (PSF). This slice relates to the out-of-focus distance of the nano-particle. In this paper, we investigate to which extent it is possible to estimate the out-of-focus distance of the nano-particle from a 2D image based on the knowledge of the 3D PSF. To this end, we compute the Cramér-Rao bound (CRB) that provides a lower bound on the error of the best estimator of the axial position

Michaël Unser

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Blind optimization of algorithm parameters for signal denoising by Monte-Carlo sure

Continuous-Domain Formulation of Inverse Problems for Composite Sparse-Plus-Smooth Signals

Variational Phase Imaging Using the Transport-of-Intensity Equation

A New Hybrid Bayesian-Variational Particle Filter With Application To Mitotic Cell Tracking

Coupled Splines for Sparse Curve Fitting

A Psf-Based Approach To Biplane Calibration In 3D Super-Resolution Microscopy

Activelets and sparsity: A new way to detect brain activation from fMRI data

Quantitative Fourier Analysis of Approximation Techniques: Part II—Wavelets

Fast Haar-Wavelet Denoising Of Multidimensional Fluorescence Microscopy Data

Least-Squares Image Resizing Using Finite Differences

Efficient Inversion of Multiple-Scattering Model for Optical Diffraction Tomography

Image Segmentation With Background Correction Using A Multiplicative Smoothing-Spline Model

Jump-Penalized Least Absolute Values Estimation of Scalar or Circle-Valued Signals

Exponential-Spline Wavelet Bases

On Regularized Reconstruction of Vector Fields

Coupled tomography and distinct-element-method approach to exploring the granular media microstructure in a jamming hourglass

Multichannel Restoration with Limited A Priori Information

Model-Based Estimation of Three-Dimensional Stiffness Parameters in Photonic-Force Microscopy

Quantitative evaluation of software packages for single-molecule localization microscopy

On the Feasibility of Axial Tracking of a Fluorescent Nano-Particle Using a Defocusing Model