Publications by Mohammad Golbabaee

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Mohammad Golbabaee

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Compressive Sampling Strategies for Multichannel Signals : Theory and Applications

Light Field Compressive Sensing in Camera Arrays

Distributed Compressed Sensing of Hyperspectral Images via Blind Source Separation

Structured Sparsity Models for Reverberant Speech Separation

Distributed Compressed Sensing for Sensor Networks Using Thresholding

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

Numerical experiments with MALDI Imaging data

Compressive Source Separation: Theory and Methods for Hyperspectral Imaging

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

SCOOP: A Real-Time Sparsity Driven People Localization Algorithm

Multichannel Compressed Sensing via Source Separation for Hyperspectral Images

Method and system for automatic objects localization

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