Publications related to Compressed Sensing For Multi-View Tracking And 3-D Voxel Reconstruction

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

The goal of this thesis is to study continuous-domain inverse problems for the reconstruction of sparse signals and to develop efficient algorithms to solve such problems computationally. The task is to recover a signal of interest as a continuous function ...

EPFL2022

Introducing Vehicle Dynamic Models in Dynamic Networks for Navigation in UAVs

Kenneth Joseph Paul

Estimation of the trajectory is a fundamental problem in robotics. Introduction of additional measurements in a robotic platform reduces the uncertainty in the trajectory estimate. The limitations on the power and payload in a UAV platform advocates for th ...

2019

Joint Estimation Of The Room Geometry And Modes With Compressed Sensing

Pierre Vandergheynst, Hervé Lissek, Helena Peic Tukuljac

Acoustical behavior of a room for a given position of microphone and sound source is usually described using the room impulse response. If we rely on the standard uniform sampling, the estimation of room impulse response for arbitrary positions in the room ...

IEEE2018

Super Resolution Phase Retrieval for Sparse Signals

Martin Vetterli, Juri Ranieri, Gilles Baechler, Amina Chebira, Miranda Krekovic

In a variety of fields, in particular those involving imaging and optics, we often measure signals whose phase is missing or has been irremediably distorted. Phase retrieval attempts to recover the phase information of a signal from the magnitude of its Fo ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018

Sparse molecular image representation

Pascal Frossard, Sofia Karygianni

Sparsity-based models have proven to be very effective in most image processing applications. The notion of sparsity has recently been extended to structured sparsity models where not only the number of components but also their support is important. This ...

Elsevier2016

Estimation Error of the Constrained Lasso

Volkan Cevher, Yen-Huan Li, Ya-Ping Hsieh, Nissim Zerbib

This paper presents a non-asymptotic upper bound for the estimation error of the constrained lasso, under the high-dimensional (

n \ll p

) setting. In contrast to existing results, the error bound in this paper is sharp, is valid when the parameter to be e ...

2016

Compressed Sensing of Memoryless Sources

Saeid Haghighatshoar

Compressed sensing is a new trend in signal processing for efficient sampling and signal acquisition. The idea is that most real-world signals have a sparse representation in an appropriate basis and this can be exploited to capture the sparse signal by ta ...

EPFL2014

Rigorous optimization recipes for sparse and low rank inverse problems with applications in data sciences

Anastasios Kyrillidis

Many natural and man-made signals can be described as having a few degrees of freedom relative to their size due to natural parameterizations or constraints; examples include bandlimited signals, collections of signals observed from multiple viewpoints in ...

EPFL2014

Jump-Sparse and Sparse Recovery Using Potts Functionals

Martin Kurt Storath

We recover jump-sparse and sparse signals from blurred incomplete data corrupted by (possibly non-Gaussian) noise using inverse Potts energy functionals. We obtain analytical results (existence of minimizers, complexity) on inverse Potts functionals and pr ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Approximate Message Passing With Consistent Parameter Estimation and Applications to Sparse Learning

Michaël Unser, Ulugbek Kamilov

We consider the estimation of an independent and identically distributed (i.i.d.) (possibly non-Gaussian) vector x is an element of R-n from measurements y is an element of R-m obtained by a general cascade model consisting of a known linear transform foll ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Compressed Sensing For Multi-View Tracking And 3-D Voxel Reconstruction

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Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

Introducing Vehicle Dynamic Models in Dynamic Networks for Navigation in UAVs

Joint Estimation Of The Room Geometry And Modes With Compressed Sensing

Super Resolution Phase Retrieval for Sparse Signals

Sparse molecular image representation

Estimation Error of the Constrained Lasso

Compressed Sensing of Memoryless Sources

Rigorous optimization recipes for sparse and low rank inverse problems with applications in data sciences

Jump-Sparse and Sparse Recovery Using Potts Functionals

Approximate Message Passing With Consistent Parameter Estimation and Applications to Sparse Learning

Compressed Sensing of Memoryless Sources

Sparse molecular image representation

Rigorous optimization recipes for sparse and low rank inverse problems with applications in data sciences

Jump-Sparse and Sparse Recovery Using Potts Functionals

Joint Estimation Of The Room Geometry And Modes With Compressed Sensing

Introducing Vehicle Dynamic Models in Dynamic Networks for Navigation in UAVs

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

Super Resolution Phase Retrieval for Sparse Signals

Approximate Message Passing With Consistent Parameter Estimation and Applications to Sparse Learning

Estimation Error of the Constrained Lasso