52 research outputs found
Compressive Imaging using Approximate Message Passing and a Markov-Tree Prior
We propose a novel algorithm for compressive imaging that exploits both the
sparsity and persistence across scales found in the 2D wavelet transform
coefficients of natural images. Like other recent works, we model wavelet
structure using a hidden Markov tree (HMT) but, unlike other works, ours is
based on loopy belief propagation (LBP). For LBP, we adopt a recently proposed
"turbo" message passing schedule that alternates between exploitation of HMT
structure and exploitation of compressive-measurement structure. For the
latter, we leverage Donoho, Maleki, and Montanari's recently proposed
approximate message passing (AMP) algorithm. Experiments with a large image
database suggest that, relative to existing schemes, our turbo LBP approach
yields state-of-the-art reconstruction performance with substantial reduction
in complexity
On Convergence of Approximate Message Passing
Approximate message passing is an iterative algorithm for compressed sensing
and related applications. A solid theory about the performance and convergence
of the algorithm exists for measurement matrices having iid entries of zero
mean. However, it was observed by several authors that for more general
matrices the algorithm often encounters convergence problems. In this paper we
identify the reason of the non-convergence for measurement matrices with iid
entries and non-zero mean in the context of Bayes optimal inference. Finally we
demonstrate numerically that when the iterative update is changed from parallel
to sequential the convergence is restored.Comment: 5 pages, 3 figure
An Overview of Multi-Processor Approximate Message Passing
Approximate message passing (AMP) is an algorithmic framework for solving
linear inverse problems from noisy measurements, with exciting applications
such as reconstructing images, audio, hyper spectral images, and various other
signals, including those acquired in compressive signal acquisiton systems. The
growing prevalence of big data systems has increased interest in large-scale
problems, which may involve huge measurement matrices that are unsuitable for
conventional computing systems. To address the challenge of large-scale
processing, multiprocessor (MP) versions of AMP have been developed. We provide
an overview of two such MP-AMP variants. In row-MP-AMP, each computing node
stores a subset of the rows of the matrix and processes corresponding
measurements. In column- MP-AMP, each node stores a subset of columns, and is
solely responsible for reconstructing a portion of the signal. We will discuss
pros and cons of both approaches, summarize recent research results for each,
and explain when each one may be a viable approach. Aspects that are
highlighted include some recent results on state evolution for both MP-AMP
algorithms, and the use of data compression to reduce communication in the MP
network
Compressive Imaging via Approximate Message Passing with Image Denoising
We consider compressive imaging problems, where images are reconstructed from
a reduced number of linear measurements. Our objective is to improve over
existing compressive imaging algorithms in terms of both reconstruction error
and runtime. To pursue our objective, we propose compressive imaging algorithms
that employ the approximate message passing (AMP) framework. AMP is an
iterative signal reconstruction algorithm that performs scalar denoising at
each iteration; in order for AMP to reconstruct the original input signal well,
a good denoiser must be used. We apply two wavelet based image denoisers within
AMP. The first denoiser is the "amplitude-scaleinvariant Bayes estimator"
(ABE), and the second is an adaptive Wiener filter; we call our AMP based
algorithms for compressive imaging AMP-ABE and AMP-Wiener. Numerical results
show that both AMP-ABE and AMP-Wiener significantly improve over the state of
the art in terms of runtime. In terms of reconstruction quality, AMP-Wiener
offers lower mean square error (MSE) than existing compressive imaging
algorithms. In contrast, AMP-ABE has higher MSE, because ABE does not denoise
as well as the adaptive Wiener filter.Comment: 15 pages; 2 tables; 7 figures; to appear in IEEE Trans. Signal
Proces
Measure What Should be Measured: Progress and Challenges in Compressive Sensing
Is compressive sensing overrated? Or can it live up to our expectations? What
will come after compressive sensing and sparsity? And what has Galileo Galilei
got to do with it? Compressive sensing has taken the signal processing
community by storm. A large corpus of research devoted to the theory and
numerics of compressive sensing has been published in the last few years.
Moreover, compressive sensing has inspired and initiated intriguing new
research directions, such as matrix completion. Potential new applications
emerge at a dazzling rate. Yet some important theoretical questions remain
open, and seemingly obvious applications keep escaping the grip of compressive
sensing. In this paper I discuss some of the recent progress in compressive
sensing and point out key challenges and opportunities as the area of
compressive sensing and sparse representations keeps evolving. I also attempt
to assess the long-term impact of compressive sensing
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