Sparse non-negative super-resolution - simplified and stabilised

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Related publications (28)

Sparse Gradient Optimization and its Applications in Image Processing

Millions of digital images are captured by imaging devices on a daily basis. The way imaging devices operate follows an integral process from which the information of the original scene needs to be estimated. The estimation is done by inverting the integra ...

EPFL2017

Scampi: a robust approximate message-passing framework for compressive imaging

Florent Gérard Krzakala, Jean François Emmanuel Barbier

Reconstruction of images from noisy linear measurements is a core problem in image processing, for which convex optimization methods based on total variation (TV) minimization have been the long-standing state-of-the-art. We present an alternative probabil ...

Iop Publishing Ltd2016

Characterization of the Solution of Linear Inverse Problems with Generalized TV Regularization

Michaël Unser

Ill-posed inverse problems are often constrained by imposing a bound on the total variation of the solution. Here, we consider a generalized version of total-variation regularization that is tied to some differential operator L. We then show that the gener ...

OSA2016

Optical Tomographic Image Reconstruction Based on Beam Propagation and Sparse Regularization

Michaël Unser, Cédric René Jean Vonesch, Demetri Psaltis, Ulugbek Kamilov

Optical tomographic imaging requires an accurate forward model as well as regularization to mitigate missing-data artifacts and to suppress noise. Nonlinear forward models can provide more accurate interpretation of the measured data than their linear coun ...

2016

A Methodology for Global Sensitivity Analysis for Time-Dependent Code Output Applied to Reflood Simulation in TRACE

Andreas Pautz, Damar Canggih Wicaksono, Omar Zerkak

Understanding properly the impact of model parameters and their interactions on the predictions is required for an appropriate model assessment. For simulation of reflooding following a LOCA, this requirement is justified because the important parameters a ...

2015

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

Michaël Unser, Nikolaos Stergiopoulos, 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

Cloaking via anomalous localized resonance. A connection between the localized resonance and the blow up of the power for doubly complementary media

Hoài-Minh Nguyên

This paper is devoted to the study of cloaking via anomalous localized resonance (CALR) in two and three dimensions in the quasistatic regime. Two key figures of CALR are (i) the localized resonance and (ii) the connection between the localized resonance a ...

Elsevier2015

Reconstruction and stability in acousto-optic imaging for absorption maps with bounded variation

Quoc Hung Nguyen

The aim of this paper is to propose for the first time a reconstruction scheme and a stability result for recovering from acousto-optic data absorption distributions with bounded variation. The paper extends earlier results in [3] and [5] on smooth absorpt ...

Academic Press Inc Elsevier Science2014

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

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Cloaking via anomalous localized resonance. A connection between the localized resonance and the blow up of the power for doubly complementary media

Hoài-Minh Nguyên

Elsevier2015