4,259 research outputs found
Fast methods for denoising matrix completion formulations, with applications to robust seismic data interpolation
Recent SVD-free matrix factorization formulations have enabled rank
minimization for systems with millions of rows and columns, paving the way for
matrix completion in extremely large-scale applications, such as seismic data
interpolation.
In this paper, we consider matrix completion formulations designed to hit a
target data-fitting error level provided by the user, and propose an algorithm
called LR-BPDN that is able to exploit factorized formulations to solve the
corresponding optimization problem. Since practitioners typically have strong
prior knowledge about target error level, this innovation makes it easy to
apply the algorithm in practice, leaving only the factor rank to be determined.
Within the established framework, we propose two extensions that are highly
relevant to solving practical challenges of data interpolation. First, we
propose a weighted extension that allows known subspace information to improve
the results of matrix completion formulations. We show how this weighting can
be used in the context of frequency continuation, an essential aspect to
seismic data interpolation. Second, we propose matrix completion formulations
that are robust to large measurement errors in the available data.
We illustrate the advantages of LR-BPDN on the collaborative filtering
problem using the MovieLens 1M, 10M, and Netflix 100M datasets. Then, we use
the new method, along with its robust and subspace re-weighted extensions, to
obtain high-quality reconstructions for large scale seismic interpolation
problems with real data, even in the presence of data contamination.Comment: 26 pages, 13 figure
Convex recovery of continuous domain piecewise constant images from non-uniform Fourier samples
We consider the recovery of a continuous domain piecewise constant image from
its non-uniform Fourier samples using a convex matrix completion algorithm. We
assume the discontinuities/edges of the image are localized to the zero
levelset of a bandlimited function. This assumption induces linear dependencies
between the Fourier coefficients of the image, which results in a two-fold
block Toeplitz matrix constructed from the Fourier coefficients being low-rank.
The proposed algorithm reformulates the recovery of the unknown Fourier
coefficients as a structured low-rank matrix completion problem, where the
nuclear norm of the matrix is minimized subject to structure and data
constraints. We show that exact recovery is possible with high probability when
the edge set of the image satisfies an incoherency property. We also show that
the incoherency property is dependent on the geometry of the edge set curve,
implying higher sampling burden for smaller curves. This paper generalizes
recent work on the super-resolution recovery of isolated Diracs or signals with
finite rate of innovation to the recovery of piecewise constant images.Comment: Supplementary material is attached with the main manuscrip
Optimization on the Hierarchical Tucker manifold - applications to tensor completion
In this work, we develop an optimization framework for problems whose
solutions are well-approximated by Hierarchical Tucker (HT) tensors, an
efficient structured tensor format based on recursive subspace factorizations.
By exploiting the smooth manifold structure of these tensors, we construct
standard optimization algorithms such as Steepest Descent and Conjugate
Gradient for completing tensors from missing entries. Our algorithmic framework
is fast and scalable to large problem sizes as we do not require SVDs on the
ambient tensor space, as required by other methods. Moreover, we exploit the
structure of the Gramian matrices associated with the HT format to regularize
our problem, reducing overfitting for high subsampling ratios. We also find
that the organization of the tensor can have a major impact on completion from
realistic seismic acquisition geometries. These samplings are far from
idealized randomized samplings that are usually considered in the literature
but are realizable in practical scenarios. Using these algorithms, we
successfully interpolate large-scale seismic data sets and demonstrate the
competitive computational scaling of our algorithms as the problem sizes grow
Applications of Compressed Sensing in Communications Networks
This paper presents a tutorial for CS applications in communications
networks. The Shannon's sampling theorem states that to recover a signal, the
sampling rate must be as least the Nyquist rate. Compressed sensing (CS) is
based on the surprising fact that to recover a signal that is sparse in certain
representations, one can sample at the rate far below the Nyquist rate. Since
its inception in 2006, CS attracted much interest in the research community and
found wide-ranging applications from astronomy, biology, communications, image
and video processing, medicine, to radar. CS also found successful applications
in communications networks. CS was applied in the detection and estimation of
wireless signals, source coding, multi-access channels, data collection in
sensor networks, and network monitoring, etc. In many cases, CS was shown to
bring performance gains on the order of 10X. We believe this is just the
beginning of CS applications in communications networks, and the future will
see even more fruitful applications of CS in our field.Comment: 18 page
k-Space Deep Learning for Reference-free EPI Ghost Correction
Nyquist ghost artifacts in EPI are originated from phase mismatch between the
even and odd echoes. However, conventional correction methods using reference
scans often produce erroneous results especially in high-field MRI due to the
non-linear and time-varying local magnetic field changes. Recently, it was
shown that the problem of ghost correction can be reformulated as k-space
interpolation problem that can be solved using structured low-rank Hankel
matrix approaches. Another recent work showed that data driven Hankel matrix
decomposition can be reformulated to exhibit similar structures as deep
convolutional neural network. By synergistically combining these findings, we
propose a k-space deep learning approach that immediately corrects the phase
mismatch without a reference scan in both accelerated and non-accelerated EPI
acquisitions. To take advantage of the even and odd-phase directional
redundancy, the k-space data is divided into two channels configured with even
and odd phase encodings. The redundancies between coils are also exploited by
stacking the multi-coil k-space data into additional input channels. Then, our
k-space ghost correction network is trained to learn the interpolation kernel
to estimate the missing virtual k-space data. For the accelerated EPI data, the
same neural network is trained to directly estimate the interpolation kernels
for missing k-space data from both ghost and subsampling. Reconstruction
results using 3T and 7T in-vivo data showed that the proposed method
outperformed the image quality compared to the existing methods, and the
computing time is much faster.The proposed k-space deep learning for EPI ghost
correction is highly robust and fast, and can be combined with acceleration, so
that it can be used as a promising correction tool for high-field MRI without
changing the current acquisition protocol.Comment: To appear in Magnetic Resonance in Medicin
A Super-Resolution Framework for Tensor Decomposition
This work considers a super-resolution framework for overcomplete tensor
decomposition. Specifically, we view tensor decomposition as a super-resolution
problem of recovering a sum of Dirac measures on the sphere and solve it by
minimizing a continuous analog of the norm on the space of measures.
The optimal value of this optimization defines the tensor nuclear norm. Similar
to the separation condition in the super-resolution problem, by explicitly
constructing a dual certificate, we develop incoherence conditions of the
tensor factors so that they form the unique optimal solution of the continuous
analog of norm minimization. Remarkably, the derived incoherence
conditions are satisfied with high probability by random tensor factors
uniformly distributed on the sphere, implying global identifiability of random
tensor factors
Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction
Following the success of deep learning in a wide range of applications,
neural network-based machine learning techniques have received interest as a
means of accelerating magnetic resonance imaging (MRI). A number of ideas
inspired by deep learning techniques from computer vision and image processing
have been successfully applied to non-linear image reconstruction in the spirit
of compressed sensing for both low dose computed tomography and accelerated
MRI. The additional integration of multi-coil information to recover missing
k-space lines in the MRI reconstruction process, is still studied less
frequently, even though it is the de-facto standard for currently used
accelerated MR acquisitions. This manuscript provides an overview of the recent
machine learning approaches that have been proposed specifically for improving
parallel imaging. A general background introduction to parallel MRI is given
that is structured around the classical view of image space and k-space based
methods. Both linear and non-linear methods are covered, followed by a
discussion of recent efforts to further improve parallel imaging using machine
learning, and specifically using artificial neural networks. Image-domain based
techniques that introduce improved regularizers are covered as well as k-space
based methods, where the focus is on better interpolation strategies using
neural networks. Issues and open problems are discussed as well as recent
efforts for producing open datasets and benchmarks for the community.Comment: 14 pages, 7 figure
Reconstruction by Calibration over Tensors for Multi-Coil Multi-Acquisition Balanced SSFP Imaging
Purpose: To develop a rapid imaging framework for balanced steady-state free
precession (bSSFP) that jointly reconstructs undersampled data (by a factor of
R) across multiple coils (D) and multiple acquisitions (N). To devise a
multi-acquisition coil compression technique for improved computational
efficiency.
Methods: The bSSFP image for a given coil and acquisition is modeled to be
modulated by a coil sensitivity and a bSSFP profile. The proposed
reconstruction by calibration over tensors (ReCat) recovers missing data by
tensor interpolation over the coil and acquisition dimensions. Coil compression
is achieved using a new method based on multilinear singular value
decomposition (MLCC). ReCat is compared with iterative self-consistent parallel
imaging (SPIRiT) and profile encoding (PE-SSFP) reconstructions.
Results: Compared to parallel imaging or profile-encoding methods, ReCat
attains sensitive depiction of high-spatial-frequency information even at
higher R. In the brain, ReCat improves peak SNR (PSNR) by 1.11.0 dB over
SPIRiT and by 0.90.3 dB over PE-SSFP (meanstd across subjects;
average for N=2-8, R=8-16). Furthermore, reconstructions based on MLCC achieve
0.80.6 dB higher PSNR compared to those based on geometric coil
compression (GCC) (average for N=2-8, R=4-16).
Conclusion: ReCat is a promising acceleration framework for
banding-artifact-free bSSFP imaging with high image quality; and MLCC offers
improved computational efficiency for tensor-based reconstructions.Comment: To be published in Magnetic Resonance in Medicine.
http://onlinelibrary.wiley.com/doi/10.1002/mrm.26902/abstrac
Value function approximation via low-rank models
We propose a novel value function approximation technique for Markov decision
processes. We consider the problem of compactly representing the state-action
value function using a low-rank and sparse matrix model. The problem is to
decompose a matrix that encodes the true value function into low-rank and
sparse components, and we achieve this using Robust Principal Component
Analysis (PCA). Under minimal assumptions, this Robust PCA problem can be
solved exactly via the Principal Component Pursuit convex optimization problem.
We experiment the procedure on several examples and demonstrate that our method
yields approximations essentially identical to the true function.Comment: arXiv admin note: substantial text overlap with arXiv:0912.3599 by
other author
Multi-dimensional imaging data recovery via minimizing the partial sum of tubal nuclear norm
In this paper, we investigate tensor recovery problems within the tensor
singular value decomposition (t-SVD) framework. We propose the partial sum of
the tubal nuclear norm (PSTNN) of a tensor. The PSTNN is a surrogate of the
tensor tubal multi-rank. We build two PSTNN-based minimization models for two
typical tensor recovery problems, i.e., the tensor completion and the tensor
principal component analysis. We give two algorithms based on the alternating
direction method of multipliers (ADMM) to solve proposed PSTNN-based tensor
recovery models. Experimental results on the synthetic data and real-world data
reveal the superior of the proposed PSTNN
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