February 25 & 26, 2009
Location: Duke University
Sponsored by the AFRL ATR Center
Co-sponsored by AFOSR, ARO, DARPA, NGA and ONR
This workshop brought together leading experts in the new field of compressive sensing (CS). The meeting focused on new theory, algorithms and applications of CS. In addition to having talks from many of the leading CS researchers from academia, there were talks from the members of the US government and industry, on directions for this new technology, including future research directions and programs.
The meeting was jointly organized by Lawrence Carin (Duke) and Gregory Arnold (AFRL), who are affiliated with the AFRL ATR Center in Dayton, Ohio.
The local Duke hosts for the workshop were Lawrence Carin, David Brady, Mauro Maggioni, Xiaobai Sun, and Rebecca Willet.
University of Maryland
Acquiring the reflectance field of real objects is an important
problem that is often encountered for solving several imaging, vision
and graphics tasks such as relighting. Typical methods for capturing
such reflectance fields are based on acquiring images with a single
light source 'ON' in each image. Recently, it has been shown that
using multiple light sources per each acquired image and performing a
linear inversion in order to recover the reflectance field results in
higher signal to noise ratios of the captured reflectance fields.
Nevertheless, the number of images to be acquired to infer the
reflectance field remains identical to the number of illumination
sources. Here, we describe a method for acquiring accurate reflectance
fields of objects with significantly lower number of captured images
than the number of illumination sources. This reduction in the number
of required images is achieved by exploiting recent developments in
compressive sensing, which essentially show that if the signal to be
acquired is sparse in some basis, then the signal can be accurately
reconstructed using sub-Nyquist linear samples of the signal. We
empirically show that the reflectance fields are sparse in the Haar
basis. We then develop a scheme for capturing reflectance fields using
multiplexed illumination, thereby achieving the signal-to-noise ratio
advantages of multiplexed illumination and use a compressive sensing
based recovery algorithm to infer reflectance fields. This is joint
work with Dr. Veeraraghavan (MERL), Prof. Chellappa (UMD) and Prof.
Raskar (MIT).
UCLA
Image guided interventions via ultrasound imaging have become
the standard of care for many surgical procedures. A majority of medical
care-providers utilize low resolution ultrasound units. In addition,
many office-based or emergency department procedures are performed using
generic (non-specialized) needles. Therefore, the quality of the imagery
obtained by most ultrasound units does not allow for clear and concise
visualization of a regular needle during many needle-based procedures.
The inability to clearly see the tip of a needle in relation to the
object of interest (e.g., a vein, artery, or mass) makes such image
guided interventions less accurate. In this paper, we propose a new and
improved framework for tracking needles in ultrasound image frames. The
problem can be easily transformed to a segmentation problem, which needs
to be solved in real time. The core of our method is to employ the split
Bregman iterations [T. Goldstein and S. J. Osher, 2008] on solving the
weighted geometric active contour (WGAC) model [X. Bresson et al, 2007].
After a simple change of variables, the WGAC model can be converted to a
standard L1-minimization problem, which can then be solved rather
efficiently by Bregman iterations [W. Yin et al, 2008].
Duke University
This poster describes spectrally adaptive signal segmentation with a
compressive sensing architecture. Total variation minimization is used to
reconstruct a three-dimensional (3D) data cube from a two-dimensional
(2D) snapshot. A priori knowledge of spectral signatures found in
a sample is used for image segmentation. We apply this algorithm
to fluorescence microscopy data acquired from a snapshot spectral
imager. The instrument records 32 spectral channels that span the
spectral range between 450nm and 750nm with 10nm spectral resolution. We
report on reconstructions for both static and dynamic samples used in
fluorescence microscopy.
UCLA
The central problem for tomographic reconstruction is to reconstruct a
clean and faithful image from very limited projections and therefore
reduce the radiation dose. Since the Fourier slice theorem gives us a
linear relationship between the projection data and the medical image,
the reconstruction of image becomes a standard compressive sensing type
of problem, that is to say, we want to reconstruct the best result
(defined by
certain criteria) from limited number of linear measurements of the
image.
This leads to a constrained optimization problem and we will address a
fast
and efficient scheme to solve it. The numerical results demonstrate that
this method strongly outperforms the conventional tomographic
reconstruction
in terms of the reduced radiation dose and the quality of the
reconstruction.
University of Massachusetts, Dartmouth
This work addresses the problem of 2-D field directionality estimation
using short uniform linear array. The field directionality in the
proposed model is represented as a compressible signal in the
frequency-wavenumber domain, motivating the application of the
compressive sensing (CS) theory to the addressed problem. The spatial
interpretation of the CS approach, denoted as spatial CS (SCS), provides
a high azimuth resolution for a fixed linear array. Moreover, the SCS
provides an efficient way to exploit the spatial diversity achieved by
the changing array orientation or distributed networks to resolves the
left-right ambiguity of linear array and to improve the estimation
performance in the endfire regions. Simplicity of implementation
comparing to other methods, is an additional advantage of the proposed
approach. The performance of the SCS approach for the field
directionality estimation was evaluated via simulation and it is shown
that it outperforms other tested methods.
Radboud University (The Netherlands)
An effective way to increase the noise robustness of
automatic speech recognition is to label noisy speech features as
either reliable or unreliable (missing) prior to decoding, and to
replace (impute) the missing ones by clean speech estimates.
Work in Compressive Sensing has shown signals can be reconstructed
using relatively few measurements provided the signal is known to be
sparse in an appropriate basis. We sparsely represent spectrographic
representations of speech in an overcomplete basis of exemplar (clean)
speech signals using only the reliable speech features. The missing
elements of the speech signals are then obtained by projecting the
sparse representation in the basis. Experiments on spoken digit
recognition show that this imputation technique greatly outperforms
other imputation techniques for noise robust speech recognition.
Princeton University
We consider deterministic compressive sensing matrices where
the columns are discrete chirp waveforms from an overcomplete
dictionary, and we show that these matrices satisfy a statistical
variant of the Restricted Isometry Property. We will then describe an
algorithm for recovery of k-sparse signals that is resilient to noise,
and has complexity kN logN where N is the number of measurements. The
original motivation for our work was the application to active sensing,
specifically radar applications. However we have recently developed and
interesting connection to A/D conversion through analysis of the NYFR
receiver developed by L3 Communications as part of the A-to-I program at
DARPA. When the NYFR receiver samples a wideband signal at the level
crossings of a chirp waveform, the samples of continuous time sinusoids
are discrete chirp sequences, and so the sparse narrowband recovery
problem amounts to determining a small number of chirps that agree with
the sampled data. This reduces to essentially the same problem that we
explored in the original radar application.
Boston University
This paper presents a method for imaging of moving targets with a SAR
sensor by treating the problem as one of spatial reflectivity signal
inversion over an overcomplete dictionary of target velocities. Since
SAR sensors returns can be related to the spatial frequency domain
projections of the scattering field, we exploit insights from
compressed sensing theory to show that moving targets can be
effectively imaged with a small number of transmitters and receivers
randomly dispersed within a narrow forward cone around the scene of
interest. Existing approaches to dealing with moving targets in SAR
solve a coupled non-linear problem of target scattering and motion
estimation typically through matched filtering. In contrast, by using
an overcomplete dictionary approach we effectively linearize the
forward model and solve the moving target problem as a larger,
regularized inversion problem subject to sparsity constraints.
Army Research Laboratory
The U.S. Army Research Laboratory (ARL), as part of a mission and
customer funded exploratory program, has developed a new low-frequency,
ultra-wideband (UWB) synchronous impulse reconstruction (SIRE) synthetic
aperture radar (SAR). The radar has been used as a testbed to support
proof-of-concept demonstration for several programs for detection of
concealed targets.
We will present the recursive sidelobe minimization (RSM)
technique - which is based on compressive sensing principle - when
combined with the standard SAR image formation would result in
significant reduction in sidelobes and noise in resulting SAR imagery.
Although the technique is developed using the ARL UWB SIRE radar in
forward-looking mode, it is also applicable for other radar
configuration such as side-looking SAR.
Princeton University
We develop efficient compressive sensing algorithms based on
expander graphs for which the number of measurements is optimal. The
recovery algorithm is based on key properties of expander graphs: first,
the restricted isometry property with respect to the l1 norm (RIP1)
which is a geometric view of expander graphs; and second, the excessive
unique neighborhood property which is a combinatorial view of expander
graphs. We show that these two properties imply that not only basis
pursuit algorithm works with the expander graph, but also that there
exists a very simple combinatorial algorithm which recovers any k-sparse
signal in about k (sparsity level) very simple iterations. We also show
that by tolerating a small penalty on the number of measurements (and
not on the number of recovery iterations) we can use known constructions
of expander graphs to come up with explicit measurement matrices for
this method. Finally we show how the algorithm can be generalized to
approximate compressible signals and noisy measurements, with very high
precision with respect to the l1 metric.
UCLA
The previously introduced Split Bregman method is an
iterative scheme for solving L1 regularized problems, and is
particularly well suited for problems involving total variation and
Besov regularizers. This presentation will explore applications of
this type of scheme, including compressed sensing MRI. We shall also
discuss several novel applications of the Split Bregman method,
including image segmentation and surface reconstruction from
unorganized data points,
University of Maryland
Synthetic Aperture Radar (SAR) is an imaging modality that provides a
high resolution map of the spatial distribution of targets and terrain
based on their response to transmitted electromagnetic waveforms. In
this presentation, we will introduce a new SAR imaging scheme based on
compressing the number of transmitted waveforms. We will show that if
the target reflectivity function is assumed to be sparse in some domain,
one can reconstruct a good estimate of the reflectivity profile using a
new image formation algorithm that relies on using far fewer number of
waveforms than conventional systems do. Some applications of this
compressed aperture Radar will be discussed in the talk. This is joint
work with Glenn Easley, Dennis Healy, and Rama Chellappa.
Caltech
In this talk, we will investigate the fundamental "balancedness"
properties of linear subspaces and discuss the applications of these
properties in the emerging field of compressive sensing. First, we will
give the definitions of several "balancedness" properties for linear
subspaces and show the respective connections between them and compressive
sensing. Then using tools from high-dimensional integral geometry and
geometric probability, we establish a unified framework for analyzing
these "balancedness" properties, and give sharp performance bounds for
the "balancedness" a linear subspace can achieve under these different
notions of "balancedness". We will further discuss the applications of
this analytical framework in the analysis and design of (new) sparse
signal recovery algorithms for compressive sensing. This work concerns
fundamental properties of linear subspaces and may be of independent
mathematical interest.
University of Florida
We present two belief propagation (BP) based sparse Bayesian learning
(SBL) algorithms, referred to as the SBL-BP and the modified SBL-BP
algorithm, to recover sparse transform coefficients in large scale
compressed sensing problems. Both algorithms are based on a widely-used
hierarchical Bayesian model, which is turned into a factor graph so
that BP can be applied to achieve computational efficiency. We prove
that the messages in BP are Gaussian probability density functions
and therefore, we only need to update their means and variances
when we update the messages. The computational complexity of both
algorithms is proportional to the number of transform coefficients,
allowing the algorithms to deal with large scale compressed sensing
problems efficiently. Numerical examples are provided to demonstrate
the effectiveness of the algorithms and to show that the modified
SBL-BP algorithm performs similarly to the SBL-BP algorithm but the
former is computationally faster than the latter.
* John Hopkins University
In this work, we propose a novel scheme of layered compressed sensing
(LaCoS) for robust transmission of video signals over packet loss
channels. In the proposed transmission system, the encoder consists of a
base layer and an enhancement layer. The base layer is a conventionally
encoded bitstream and transmitted without any error protection. The
additional enhancement layer is a stream of compressed measurements
taken across slices of video signals for error-resilience. The decoder
generates side information that is a measurement vector of the corrupted
base layer and then employs a sparse recovery with this side information
to recover lost packets. By exploiting the side information at the
decoder, the enhancement layer is required to transmit a minimal amount
of compressed measurements that is only proportional to the amount of
lost packets. Simulation results show that both compression efficiency
and error-resilience capacity of the proposed scheme are competitive
with those of other state-of-the-art robust transmission methods, in
which Wyner-Ziv coders often generate an enhancement layer.
* This work has been supported in part by the National Science Foundation under Grant CCF-0728893.
* John Hopkins University
In this work, we propose a novel framework called Distributed Compressed
Video Sensing (DisCoS). Like other Distributed Video Coding (DVC) schemes,
in which video frames are often intraframe-coded and interframe-decoded
to exploit temporal correlation among them, the proposed scheme also
samples each frame independently and recovers them by exploiting
the interframe sparsity with side information. In particular, the
encoder acquires two types of compressed measurements: block-based
measurements (or local measurements) and frame-based measurements
(or global measurements). The decoder first generates a prediction of
the current frame from previously reconstructed frame(s) and the local
measurements. The key observation is that a block in the current frame
can be sparsely represented by a linear combination of few neighboring
blocks in previously reconstructed frame(s), enabling it to be predicted
from its local measurements by soling the l-1 minimization. The process
of block prediction follows a similar idea of motion estimation at the
decoder side in other state-of-the-art DVC schemes. Then, the decoder
generates global measurements of the prediction error from those of the
current frame and the predicted one. As the prediction error is often
very sparse, it can be recovered from these measurements by solving the
l-1 minimization. Simulation results show that DisCoS significantly
outperforms the intraframe-sensing and intraframe-sparse recovery
scheme. It is even comparable to a scheme of intraframe-sensing and
interframe-sparse recovery with oracle motion vectors available at
the decoder. In addition, unlike all other previous DVC schemes, the
DisCoS can perform encoding operation in the analog domain with very
low-complexity, making it a promising candidate for applications where
the sampling process is very expensive, e.g., in Terahertz imaging.
* This work has been supported in part by the National Science Foundation under Grant CCF-0728893.
* John Hopkins University
We extend our previous work of Structurally Random Matrices to
propose a fast and efficient compressive sampling for two dimensional
signals such as natural images. Unlike other previous compressive
sampling algorithms that often regard sensing signals as 1-d signals,
the proposed approach uses separable operators to sample rows and
columns of an image independently. Let X be the sensing image. Then,
the separable measurement process can be mathematically represented
as D1 F1 R1 XR2 F2 D2 , where:
* This work has been supported in part by the National Science Foundation under Grant CCF-0728893.
Vanderbilt University
There are two main approaches in compressed sensing: the geometric
approach and the combinatorial approach. In this talk, we will introduce
an information theoretic approach and construct a sequence of binary
sampling vectors to determine a sparse signal. Unlike other approaches,
ours is adaptive in the sense that each sampling vector depends on the
previous sample. The number of measurements we need is no more than O(k
log n) and the reconstruction is O(k).
Duke University
As an old problem, random sensor array has been considered for
decades. The main motivation for the use of random or non-uniform arrays
has typically been the goal of achieving high-resolution sensing while
reducing sensing costs. The recently developed compressive sensing (CS)
is a framework in which one attempts to measure a signal in a compressive
mode, implying that fewer total measurements are required. In this
poster, the random sensor arrays are examined from a CS perspective. It
is demonstrated that the natural random-array projections manifested
by the media Green's function are consistent with the projection type
measurements associated with CS. This linkage allows the use of existing
CS theory to quantify the performance of random arrays, of interest for
array design. The analysis as well as the numerical simulation examples
demonstrate that the CS theory is applicable to arrays in vacuum as
well as in the presence of a surrounding media; further, the presence
of a surrounding media with known properties may be used to improve
array performance.
UCLA
We consider the problem of inverting the heat equation, where known
point-value samples at time T are used to estimate the initial
condition. The initial condition is assumed to be sparse. We solve
the problem using compressed sensing techniques, formulating the
problem as an L1 optimization and solving it with the linearized
Bregman method. Examples in both one and two spatial dimensions are
shown. Finally, we show how our approach may be adapted to solve some
similar problems.
Lockheed-Martin
We consider the application of compressive imaging theory to increase
the performance of current imaging sensors. Spatial multiplexing
sensors can increase the effective field-of-view (FOV) of the sensor
without loss of resolution. We present an optical architecture which is
compressive in nature and results in 2-orders of magnitude increased FOV
when compressive sensing theory is applied to detect moving targets.
Building on this compressive imaging model, we then turn to the time
multiplexing case. Smart Focal-Plane-Array (FPA) technology is assumed,
to integrate light at varying time-constants throughout the scene. The
result is the ability to track a moving object during the integration
time of the sensor. These simulated examples highlight the ability to
increase current sensor performance significantly by designing sensors
and exploitation tasks simultaneously with Compressive Imaging as the
underlying theory.
Duke University
We propose a method for projecting N-dimensional vectors in
Euclidean space onto the set of K-sparse vectors. The method is
based on simple operations requiring time on the order of
O(N*log(N)). We also consider the case of projecting onto the set of
K-block-sparse vectors and extend the method to perform the
projection in time O(B*log(B),N), where B is the number of blocks.
Together with projections from linear algebra, the proposed methods
yield efficient algorithms for CS reconstructions. We present
experimental results on large images and compare to state-of-the-art
algorithms.
Stanford University
Compressed sensing is a robust method for recovering sparse signals
that can also be used in array imaging. In this work we present a
framework to assess the results of imaging using compressed sensing and
other previously developed approaches. We will show various numerical
simulations and interpret those results with analytical ones.
Duke University
We introduce a probabilistic formulation of group LASSO and employ
it as a block-sparse prior for Bayesian compressive sensing (CS).
The resulting Bayesian group LASSO algorithm retains the robustness of
Bayesian CS and, in addition, exploits the inter-dependency between the
sparse coefficients to achieve accurate signal reconstruction based on
a smaller number of measurements. The algorithm effectively reduces the
signal dimensionality by squeezing strongly dependent coefficients into
a group, and achieves computational efficiency by performing calculation
at the level of groups versus individual coefficients. We compare the
proposed algorithm, in terms of reconstruction performance as well as
time complexity, to state-of-the-art CS algorithms.
Duke University
We present performance bounds for compressed sensing in the presence
of Poisson noise and shows that, for sparse or compressible signals,
they are within a log factor of known lower bounds on the risk. The
signal-independent and bounded noise models used in the literature to
analyze the performance of compressed sensing do not accurately model
the effects of Poisson noise. However, Poisson noise is an appropriate
noise model for a variety of applications, including low-light imaging,
in which sensing hardware is large or expensive and limiting the number
of measurements collected is important. We will show how a feasible,
positivity-preserving sensing matrix can be constructed and prove a
concentration-of-measure inequality for these matrices. We then show
that minimizing an objective function consisting of a negative Poisson
log likelihood term and a penalty term which could be used as a measure
of signal sparsity results in near-minimax rates of error decay.
Duke University
A fundamental challenge in applying compressed sensing theory to
practical imaging systems is that physical constraints typically make it
infeasible to actually measure many of the random projections described
in the literature, and therefore, innovative and sophisticated imaging
systems must be carefully designed to effectively exploit CS theory.
In this work, we propose compressive imaging techniques for improving
the performance of video imaging systems in the presence of constraints
on the focal plane array size. In particular, we describe a novel yet
practical approach that combines coded aperture imaging to enhance pixel
resolution with superimposing subframes of a scene onto a single focal
plane array to increase field of view. Specifically, the proposed method
superimposes coded observations and uses wavelet-based sparsity recovery
algorithms to reconstruct the original subframes. We demonstrate the
effectiveness of this approach by reconstructing with high resolution
the constituent images of a video sequence.
Ohio State University
We consider the relationship between parameter estimation of an additive
model and sparse inversion of an under-determined matrix (dictionary) in
a linear system. The dictionary is constructed by sampling parameters of
the additive model and does not satisfy the Restricted Isometry Property
(RIP) of Compressive Sensing. Parameters and model order are estimated
using regularized least-squares inversion with lp norm penalty, where
p≤1. We investigate equi-spaced and Fisher information inspired
parameter sampling methods for dictionary construction. Examples are
presented quantifying parameter estimation performance for the different
sampling methods and for different sampling densities.
Duke University
This paper analyzes the role of coded apertures and compressive-sensing
based inference with x-ray transform measurements to improve data
efficiency and reconstruction fidelity. While x-ray tomography is the
canonical example, diverse physical measurements can be modeled by x-ray
transform measurements. Our group has previously demonstrated the use
of coding in reference structure tomography (RST) and coded aperture
snapshot spectral imaging (CASSI). This paper investigates coding
strategies in these applications and extends this approach in proposing
compressive x-ray tomography based on the use of coding concepts.
Boston Univeristy
We will describe a novel algorithm based on thresholding the solution to
basis pursuit for support recovery and approximation. Our result offers
a sharp characterization in that neither the SNR nor the sparsity ratio
can be significantly improved in comparison to the information
theoretic/Max-Likelihood bounds. Our idea is to adopt a perturbation
approach to the noiseless problem. Keeping the number of measurements
fixed (at the level achievable for noiseless recovery) we increase the
noise and characterize the maximum noise level for which support
recovery fails.
Rice University
We demonstrate that the recovery rate of l1-minimization on signals
with fast decaying distribution of nonzero values can be enhanced
by applying a simple, novel iterative support detection strategy.
Preliminary theoretical and experimental results, as well as the
limitation of the strategy, are presented.
Columbia University
We describe a fast algorithm for sparse reconstruction. The
algorithm is divided into two stages that are performed repeatedly. In
the first stage, "shrinkage" yields an estimate of the subset of
variables likely to be nonzero in an optimal solution. Restricting the
decision variables to this subset and fixing their signs at their
current values results in a smooth quadratic problem that is solved in
the second phase. Our method also embeds this basic two-stage
algorithm in a continuation (homotopy) approach. Our implementation of
this method exhibits state-of-the-art performance both in terms of its
speed and its ability to recover sparse signals. It can even recover
signals that are not as sparse as required by current compressive
sensing theory.
Columbia University
Because information such as boundaries of organs is very sparse
in most MR images, compressed sensing makes it possible to reconstruct the
same MR image from a very limited set of measurements significantly reducing
the MRI scan duration. In order to do that however, one has to solve the
difficult problem of minimizing nonsmooth functions on large data sets. To
handle this, we propose an efficient algorithm that jointly minimizes the
L1 norm, total variation, and a least squares measure, one of the most
powerful models for compressive MR imaging. Our algorithm is based upon an
iterative operator-splitting framework. The calculations are accelerated by
continuation and takes advantage of fast wavelet and Fourier transforms
enabling our code to process MR images from actual real life applications.
We show that faithful MR images can be reconstructed from a subset that
represents a mere 20 percent of the complete set of measurements.
UC Irvine
In this work, we study compressive sensing (CS) in bioluminescence
tomography (BLT), in particularly based on radiative transfer equation
(RTE). BLT is an emerging and promising technique for molecular imaging
in which one tries to reconstruct the distribution of bioluminescent
sources through boundary measurements. Although the source localization
problem can be casted as a linear problem, the reconstruction is still
very challenging due to the severe ill-posedness. Since sources in
BLT are usually sparse in space, compressive sensing method would be a
natural solution. However, directly using Green's function of RTE as the
basis and standard measurement data for reconstruction will not satisfy
the restricted isometry property (RIP) which is crucial for the success
of CS. We propose an algorithm to transform the standard setup into a
new system that satisfies the RIP. Hence, with compressive sensing method,
we produce much improved results over standard reconstruction method.
Bin Dong, Eric Savitsky and Stanley Osher -
A Novel Method for Enhanced Needle Localization Using
Ultrasound-Guidance
Christy Fernandez - Cull
Image Segmentation with a Compressive Snapshot Spectral Imager
David Mao -
Equally-Sloped Tomographic Reconstruction and Its Application to
Radiation Dose Reduction.
Igal Bilik -
Spatial Compressive Sensing Approach for Field Directionality Estimation
Via Spatial Diversity.
Jort F. Gemmeke -
Missing Data Imputation for Noise Robust Speech Recognition
Using Compressive Sensing
Lorne Applebaum -
Chirp Sensing and A/D Conversion
Ivana Stojanovic and W. Clem Karl -
An Overcomplete Dictionary Approach to Imaging of Moving Targets
with Multistatic SAR
Lam Nguyen -
SAR Imaging Technique for Reduction of Sidelobes and Noise
Sina Jafarpour -
Compressed Sensing Using High-quality Expander Graphs: Optimal
Measurements, Efficient Recovery, Explicit Construction, and Noise
Tolerance.
Tom Goldstein -
Split Bregman Schemes for L1 Regularized Problems
Vishal Patel -
Compressive Synthetic Aperture Radar Imaging
Weiyu Xu -
The Balancedness Properties of Linear Subspaces and their
Applications in Compressive Sensing
Xing Tan -
Computationally Efficient Sparse Bayesian Learning via Belief
Propagation.
Thong T. Do*, Yi Chen* , Dzung T. Nguyen* , Nam Nguyen* , Lu Gan** and Trac D. Tran** -
Robust Video Transmission Using Layered Compressed Sensing
** Brunel University (UK)
Thong T. Do*, Yi Chen* , Dzung T. Nguyen* , Nam Nguyen* , Lu Gan** and Trac D. Tran** -
Distributed Compressed Video Sensing
** Brunel University (UK)
Thong T. Do*, Yi Chen* , Dzung T. Nguyen* , Nam Nguyen* , Lu Gan** and Trac D. Tran** -
Fast and Efficient Compressive Imaging Using Separable Measurement Ensembles
** Brunel University (UK)
The proposed measurement ensemble is equivalent to (i) randomly flipping
signs of rows and columns of the target signal, (ii) applying a separable
orthornormal transform to the randomized image and finally (iii) randomly
sampling rows and columns of the transformed image independently to
get compressed measurements. Note that the complexity of the proposed
scheme is similar to that of the separable orthornormal transform that
is very low, speeding up both sensing and recovery processes. Simulation
results show that performance of the proposed scheme is only 1-2 dB less
than the optimal performance. We also introduce an application of this
separable measurement ensemble for efficient realization of compressive
image sensors.
Haichao Wang -
Sequential Compressed Sensing
Lawrence Carin, Bin Guo, Dehong Liu, and Wenbin Lin -
On Compressive Sensing for the Random Sensor Array Design
Yingying Li -
Heat Source Identification Based on Compressed Sensing
Robert Muise - A Compressive Imaging Approach for Tracking Targets in Multiplexed Data
Xuejun Liao, Hui Li, and Lawrence Carin - Fast Projections onto the Set of Sparse Signals
Anwei Chai - Compressed Sensing and Imaging: a Comparative Study
Haojun Chen - Bayesian Group LASSO for Compressive Sensing
Maxim Raginsky and Rebecca M. Willett - Minimax Risk Bounds for Poisson Compressed Sensing
Zachary Harmany, Roummel Marcia, and Rebecca Willett - Compressive Coded Aperture Imaging
Christian Austin - On the Relation Between Sparse Sampling and Parametric Estimation
Kerkil Choi - Coded Aperture Compressive Computed Tomography
Manqi Zhao, Venkatesh Saligrama - Thresholded Basis Pursuit: A Linear Programming Approach for Support Recovery
Wotao Yin - Enhanced Compressed Sensing Based on Iterative Support Detection
Zaiwen Wen - A Fast Algorighm For Sparse Reconstruction Based On Shrinkage, Subspace Optimization and Continuation
Shiqian Ma - An Efficient Algorithm for Compressed MR Imaging using Total Variation and Wavelets
Hao Gao and Hongkai Zhao - Compressive Sensing in Bioluminescence Tomography Based on Radiative Transfer Equation