Jin Tan

Works (6)

Updated: July 5th, 2023 15:43

2015 journal article

Compressive Hyperspectral Imaging via Approximate Message Passing

IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 10(2), 389–401.

By: J. Tan n, Y. Ma n, H. Rueda*, D. Baron n  & G. Arce *

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)
Sources: Web Of Science, NC State University Libraries
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).

By: J. Tan n, D. Baron n  & L. Dai *

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, NC State University Libraries
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.

By: J. Tan n, D. Baron n  & L. Dai *

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, NC State University Libraries
Added: August 6, 2018

2013 journal article

Signal Estimation With Additive Error Metrics in Compressed Sensing

IEEE TRANSACTIONS ON INFORMATION THEORY, 60(1), 150–158.

By: J. Tan n, D. Carmon* & D. Baron n 

author keywords: Belief propagation (BP); compressed sensing; error metric; estimation theory
Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018

2013 conference paper

Signal reconstruction in linear mixing systems with different error metrics

2013 Information Theory and Applications Workshop (ITA).

By: J. Tan n & D. Baron n 

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, NC State University Libraries
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.

By: J. Tan n, D. Carmon n & D. Baron n 

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, NC State University Libraries
Added: August 6, 2018

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