Works (6)
Updated: July 5th, 2023 15:43
2016 journal article
Compressive Hyperspectral Imaging via Approximate Message Passing
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 10(2), 389–401.
author keywords: Approximate message passing; CASSI; compressive hyperspectral imaging; gradient projection for sparse reconstruction; image denoising; two-step iterative shrinkage/thresholding; Wiener filtering
TL;DR:
The adaptive Wiener filter is modified and employed to solve for the divergence issue of AMP, and the numerical experiments show that AMP-3D-Wiener outperforms existing widely-used algorithms such as gradient projection for sparse reconstruction (GPSR) and two-step iterative shrinkage/thresholding (TwIST) given a similar amount of runtime.
(via Semantic Scholar)
doi.org (publisher)
Sources: Web Of Science, ORCID
Added: August 6, 2018
2014 journal article
Signal Estimation With Additive Error Metrics in Compressed Sensing
IEEE TRANSACTIONS ON INFORMATION THEORY, 60(1), 150–158.
author keywords: Belief propagation (BP); compressed sensing; error metric; estimation theory
Sources: Web Of Science, ORCID
Added: August 6, 2018
2014 conference paper
Signal estimation with low infinity-norm error by minimizing the mean p-norm error
2014 48th Annual Conference on Information Sciences and Systems (CISS).
TL;DR:
Numerical results show that the ℓp-norm minimizer outperforms the Wiener filter, and suggest that the optimal value of p increases with the signal dimension, and that for i.i.d. Bernoulli-Gaussian input signals, the optimal p increaseswith the percentage of nonzeros.
(via Semantic Scholar)
Sources: NC State University Libraries, ORCID
Added: August 6, 2018
2014 journal article
Wiener Filters in Gaussian Mixture Signal Estimation With l(infinity)-Norm Error
IEEE TRANSACTIONS ON INFORMATION THEORY, 60(10), 6626–6635.
author keywords: Estimation theory; Gaussian mixtures; l(infinity)-norm error; linear mixing systems; parallel Gaussian channels; Wiener filters
TL;DR:
It is proved that, in an asymptotic setting where the signal dimension N → ∞, the l∞-norm error always comes from the Gaussian component that has the largest variance, and the Wiener filter asymPTotically achieves the optimal expected l ∼norm error.
(via Semantic Scholar)
Sources: Web Of Science, ORCID
Added: August 6, 2018
2013 conference paper
Signal reconstruction in linear mixing systems with different error metrics
2013 Information Theory and Applications Workshop (ITA).
TL;DR:
A simple, fast, and highly general algorithm that reconstructs the signal by minimizing the user-defined error metric and can be adjusted to minimize the ℓ∞ error, which is not additive.
(via Semantic Scholar)
Sources: NC State University Libraries, ORCID
Added: August 6, 2018
2012 conference paper
Optimal estimation with arbitrary error metrics in compressed sensing
2012 IEEE Statistical Signal Processing Workshop (ssp), 588–591.
TL;DR:
This paper proposes a simple, fast, and general algorithm that estimates the original signal by minimizing an arbitrary error metric defined by the user, and describes a general method to compute the fundamental information-theoretic performance limit for any well-defined error metric.
(via Semantic Scholar)
Sources: NC State University Libraries, ORCID
Added: August 6, 2018
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