Publications by Mohammad Golbabaee

Model-based super-resolution reconstruction of T-2 maps

Jean-Philippe Thiran, Reto Meuli, Mohammad Golbabaee, Tobias Kober, Tom Hilbert, Gian Franco Piredda, Wajiha Bano

Purpose High-resolution isotropic T-2 mapping of the human brain with multi-echo spin-echo (MESE) acquisitions is challenging. When using a 2D sequence, the resolution is limited by the slice thickness. If used as a 3D acquisition, specific absorption rate ...

Wiley2020

Structured Sparsity Models for Reverberant Speech Separation

Hervé Bourlard, Mohammad Golbabaee, Afsaneh Asaei, Volkan Cevher

We tackle the multi-party speech recovery problem through modeling the acoustic of the reverberant chambers. Our approach exploits structured sparsity models to perform room modeling and speech recovery. We propose a scheme for characterizing the room acou ...

2014

Numerical experiments with MALDI Imaging data

Pierre Vandergheynst, Mohammad Golbabaee

This article does not present new mathematical results, it solely aims at discussing some numerical experiments with MALDI Imaging data. However, these experiments are based on and could not be done without the mathematical results obtained in the UNLocX p ...

Springer2014

Compressive Source Separation: Theory and Methods for Hyperspectral Imaging

Pierre Vandergheynst, Mohammad Golbabaee

With the development of numbers of high resolution data acquisition systems and the global requirement to lower the energy consumption, the development of efficient sensing techniques becomes critical. Recently, Compressed Sampling (CS) techniques, which e ...

Institute of Electrical and Electronics Engineers2013

Light Field Compressive Sensing in Camera Arrays

Pierre Vandergheynst, Mohammad Golbabaee, Mahdad Hosseini Kamal

This paper presents a novel approach to capture light field in camera arrays based on the compressive sensing framework. Light fields are captured by a linear array of cameras with overlapping field of view. In this work, we design a redundant dictionary t ...

Ieee2012

SCOOP: A Real-Time Sparsity Driven People Localization Algorithm

Pierre Vandergheynst, Alexandre Massoud Alahi, Mohammad Golbabaee

Detecting and tracking people in scenes monitored by cameras is an important step in many application scenarios such as surveillance, urban planning or behavioral studies to name a few. The amount of data produced by camera feeds is so large that it is als ...

Springer Verlag2012

Hyperspectral Image Compressed Sensing Via Low-Rank And Joint-Sparse Matrix Recovery

Pierre Vandergheynst, Mohammad Golbabaee

We propose a novel approach to reconstruct Hyperspectral images from very few number of noisy compressive measure- ments. Our reconstruction approach is based on a convex minimiza- tion which penalizes both the nuclear norm and the l2,1 mixed-norm of the d ...

Ieee2012

Compressive Sampling Strategies for Multichannel Signals : Theory and Applications

Mohammad Golbabaee

Over the past decade researches in applied mathematics, signal processing and communications have introduced compressive sampling (CS) as an alternative to the Shannon sampling theorem. The two key observations making CS theory widely applicable to numerou ...

EPFL2012

Guaranteed recovery of a low-rank and joint-sparse matrix from incomplete and noisy measurements (Abstract)

Pierre Vandergheynst, Mohammad Golbabaee

2011

Method and system for automatic objects localization

Pierre Vandergheynst, Alexandre Massoud Alahi, Mohammad Golbabaee

A method for automatic localization of objects in a mask. The method includes building a dictionary or atoms, wherein each atom models the presence of one object at one location and iteratively determining the atom of said dictionary which is best correlat ...

2010

Multichannel Compressed Sensing via Source Separation for Hyperspectral Images

Pierre Vandergheynst, Mohammad Golbabaee

This paper describes a novel framework for compressive sampling of multichannel signals that are highly correlated across the channels. In this work, we assume few number of independent sources are generating the multichannel observations based on a linear ...

2010

Distributed Compressed Sensing of Hyperspectral Images via Blind Source Separation

Pierre Vandergheynst, Mohammad Golbabaee

This paper describes a novel framework for compressive sampling (CS) of multichannel signals that are highly dependent across the channels. In this work, we assume few number of sources are generating the multichannel observations based on a linear mixture ...

2010

Distributed Compressed Sensing for Sensor Networks Using Thresholding

Pierre Vandergheynst, Mohammad Golbabaee

Distributed compressed sensing is the extension of compressed sampling (CS) to sensor networks. The idea is to design a CS joint decoding scheme at a central decoder (base station) that exploits the inter-sensor correlations, in order to recover the whole ...

2009

Average case analysis of sparse recovery with Thresholding: New bounds based on average dictionary coherence

Pierre Vandergheynst, Mohammad Golbabaee

This paper analyzes the performance of the simple thresholding algorithm for sparse signal representations. In particular, in order to be more realistic we introduce a new probabilistic signal model which assumes randomness for both the amplitude and also ...

2008