Results 231 to 240 of about 981,446 (269)

Look ahead orthogonal matching pursuit

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011
For compressive sensing, we endeavor to improve the recovery performance of the existing orthogonal matching pursuit (OMP) algorithm. To achieve a better estimate of the underlying support set progressively through iterations, we use a look ahead strategy.
Saikat Chatterjee, Dennis Sundman, Mikael Skoglund   +2 more
openaire   +2 more sources

Reduced Look Ahead Orthogonal Matching Pursuit

2014 Twentieth National Conference on Communications (NCC), 2014
Compressed Sensing (CS) is an elegant technique to acquire signals and reconstruct them efficiently by solving a system of under-determined linear equations. The excitement in this field stems from the fact that we can sample at a rate way below the Nyquist rate and still reconstruct the signal provided some conditions are met.
Prateek Basavapur Swamy, Sooraj K. Ambat, Saikat Chatterjee, K. V. S. Hari   +3 more
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Subspace pursuit embedded in Orthogonal Matching Pursuit

TENCON 2012 IEEE Region 10 Conference, 2012
Orthogonal Matching Pursuit (OMP) is a popular greedy pursuit algorithm widely used for sparse signal recovery from an undersampled measurement system. However, one of the main shortcomings of OMP is its irreversible selection procedure of columns of measurement matrix.
Sooraj K. Ambat, Saikat Chatterjee, K.V.S. Hari   +2 more
core   +3 more sources

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Orthogonal Matching Pursuit on Faulty Circuits

IEEE Transactions on Communications, 2015
With the wide recognition that modern nanoscale devices will be error-prone, characterization of reliability of information processing systems built out of unreliable components has become an important topic. In this paper, we analyze the performance of orthogonal matching pursuit (OMP), a popular sparse recovery algorithm, running on faulty circuits ...
Yao Li 0007, Yuejie Chi, Chu-Hsiang Huang, Lara Dolecek   +3 more
openaire   +1 more source

Online search Orthogonal Matching Pursuit

2012 IEEE Statistical Signal Processing Workshop (SSP), 2012
The recovery of a sparse signal x from y= Φx, where Φ is a matrix with more columns than rows, is a task central to many signal processing problems. In this paper we present a new greedy algorithm to solve this type of problem. Our approach leverages ideas from the field of online search on state spaces.
Alejandro J. Weinstein, Michael B. Wakin
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Randomized simultaneous orthogonal matching pursuit

2015 23rd European Signal Processing Conference (EUSIPCO), 2015
In this paper, we develop randomized simultaneous orthogonal matching pursuit (RandSOMP) algorithm which computes an approximation of the Bayesian minimum mean-squared error (MMSE) estimate of an unknown rowsparse signal matrix. The approximation is based on greedy iterations, as in SOMP, and it elegantly incorporates the prior knowledge of the ...
Aqib Ejaz, Esa Ollila, Visa Koivunen
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A fast orthogonal matching pursuit algorithm

Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181), 2002
The problem of optimal approximation of members of a vector space by a linear combination of members of a large overcomplete library of vectors is of importance in many areas including image and video coding, image analysis, control theory, and statistics. Finding the optimal solution in the general case is mathematically intractable. Matching pursuit,
Mohammad Gharavi-Alkhansari, Thomas S. Huang   +1 more
openaire   +1 more source

Stagewise Arithmetic Orthogonal Matching Pursuit

International Journal of Wireless Information Networks, 2018
In order to improve the problems that stagewise weak orthogonal matching pursuit (SWOMP) has low reconstruction accuracy and imprecise choice of indexs selecting, an effective algorithm called stagewise arithmetic orthogonal matching pursuit (SAOMP) was proposed.
Yingying Zhang, Guiling Sun
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Sparsity and incoherence in orthogonal matching pursuit

Multidimensional Systems and Signal Processing, 2018
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yi Shen, Ruifang Hu
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Orthogonal matching pursuit for VHR image reconstruction

2012 IEEE International Geoscience and Remote Sensing Symposium, 2012
Reconstructing missing data in very high resolution (VHR) multispectral images represents a complex image processing challenge. In this paper, we propose a new method for the reconstruction of areas obscured by clouds. It is based on compressive sensing (CS) theory, which allows to find sparse signal representations in underdetermined linear equation ...
Lorenzi, Luca, Melgani, Farid, G. Mercier   +2 more
openaire   +2 more sources