Publications by Stamatios Lefkimmiatis

Structure Tensor Total Variation

We introduce a novel generic energy functional that we employ to solve inverse imaging problems within a variational framework. The proposed regularization family, termed as structure tensor total variation (STV), penalizes the eigenvalues of the structure ...

SIAM2015

Improved Variational Denoising of Flow Fields with Application to Phase-Contrast MRI Data

Nikolaos Stergiopoulos, Michaël Unser, Emrah Bostan, Orestis Vardoulis, Stamatios Lefkimmiatis

We propose a new variational framework for the problem of reconstructing flow fields from noisy measurements. The formalism is based on regularizers penalizing the singular values of the Jacobian of the field. Specifically, we rely on the nuclear norm. Our ...

IEEE Institute of Electrical and Electronics Engineers2015

High-Performance 3D Deconvolution of Fluorescence Micrographs

Michaël Unser, Stamatios Lefkimmiatis

In this work, we describe our approach of combining the most effective ideas and tools developed during the past years to build a variational 3D deconvolution system that can be successfully employed in fluorescence microscopy. In particular, the main comp ...

IEEE2014

Digital Phase Reconstruction via Iterative Solutions of Transport-of-Intensity Equation

Michaël Unser, Emrah Bostan, Stamatios Lefkimmiatis, Emmanuel Augustin Julien Froustey

We develop a variational algorithm for reconstructing phase objects from a series of bright field micrographs. Our mathematical model is based on the transport-of-intensity equation (TIE), which links the phase of a complex field to the axial derivative of ...

Ieee2014

Convex Generalizations of Total Variation Based on the Structure Tensor with Applications to Inverse Problems

Michaël Unser, Stamatios Lefkimmiatis

We introduce a generic convex energy functional that is suitable for both grayscale and vector-valued images. Our functional is based on the eigenvalues of the structure tensor, therefore it penalizes image variation at every point by taking into account t ...

Springer2013

Benchmarking Image-Processing Algorithms for Biomicroscopy: Reference Datasets and Perspectives

Michaël Unser, Daniel Sage, Cédric René Jean Vonesch, Hagay Kirshner, Stamatios Lefkimmiatis

As the field of bioimage informatics matures, the issue of the validation of image reconstruction algorithms and the definition of proper performance criteria becomes more pressing. In this work, we discuss benchmarking aspects of fluorescence microscopy q ...

EUSIPCO2013

Hessian Schatten-Norm Regularization for Linear Inverse Problems

Michaël Unser, Stamatios Lefkimmiatis, John Paul Ward

We introduce a novel family of invariant, convex, and non-quadratic functionals that we employ to derive regularized solutions of ill-posed linear inverse imaging problems. The proposed regularizers involve the Schatten norms of the Hessian matrix, which a ...

Ieee-Inst Electrical Electronics Engineers Inc2013

Constrained regularized reconstruction of X-ray-DPCI tomograms with weighted-norm

Michaël Unser, Cédric René Jean Vonesch, Masih Nilchian, Stamatios Lefkimmiatis, Peter Modregger, Marco Stampanoni

In this paper we introduce a new reconstruction algorithm for X-ray differential phase-contrast Imaging (DPCI). Our approach is based on 1) a variational formulation with a weighted data term and 2) a variable-splitting scheme that allows for fast converge ...

Optical Society of America2013

3D Poisson Microscopy Deconvolution With Hessian Schatten-Norm Regularization

Michaël Unser, Stamatios Lefkimmiatis

Inverse problems with shot noise arise in many modern biomedical imaging applications. The main challenge is to obtain an estimate of the underlying specimen from measurements corrupted by Poisson noise. In this work, we propose an efficient framework for ...

IEEE2013

Poisson Image Reconstruction With Hessian Schatten-Norm Regularization

Michaël Unser, Stamatios Lefkimmiatis

Poisson inverse problems arise in many modern imaging applications, including biomedical and astronomical ones. The main challenge is to obtain an estimate of the underlying image from a set of measurements degraded by a linear operator and further corrupt ...

Ieee-Inst Electrical Electronics Engineers Inc2013

A Projected Gradient Algorithm for Image Restoration Under Hessian Matrix-Norm Regularization

Michaël Unser, Stamatios Lefkimmiatis

We have recently introduced a class of non-quadratic Hessian-based regularizers as a higher-order extension of the total variation (TV) functional. These regularizers retain some of the most favorable properties of TV while they can effectively deal with t ...

IEEE2012

Hessian-Based Norm Regularization for Image Restoration With Biomedical Applications

Michaël Unser, Aurélien Bourquard, Stamatios Lefkimmiatis

We present nonquadratic Hessian-based regularization methods that can be effectively used for image restoration problems in a variational framework. Motivated by the great success of the total-variation (TV) functional, we extend it to also include second- ...

IEEE2012

Hessian-Based Regularization For 3-D Microscopy Image Restoration

Michaël Unser, Aurélien Bourquard, Stamatios Lefkimmiatis

We investigate a non quadratic regularizer that is based on the Hessian operator for dealing with the restoration of 3-D images in a variational framework. We show that the regularizer under study is a valid extension of the total-variation (TV) functional ...

Ieee2012

A Second-Order Extension Of Tv Regularization For Image Deblurring

Michaël Unser, Aurélien Bourquard, Zafer Dogan, Stamatios Lefkimmiatis

In this paper, we propose a novel second-order regularizer based on the maximum response of the second-order directional derivative, assuming that the image under consideration belongs to the class of piecewise-linear signals. Compared to total-variation r ...

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