ROBUST IMAGE ESTIMATION IN SIGNAL-DEPENDENT NOISE.

Sin-Horng Chen; John Murray; John F. Walkup

doi:10.1109/ICASSP.1984.1172811

ROBUST IMAGE ESTIMATION IN SIGNAL-DEPENDENT NOISE.

Sin-Horng Chen^*, John Murray, John F. Walkup

^*Corresponding author for this work

Department of Electrical and Computer Engineering

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

Several estimators for the digital restoration of a noisy image are presented. These estimators differ from those previously found in the literature in that they are robust for deviations from the assumed signal or noise statistics, and the image is assumed to have been corrupted by signal-dependent or nonlinear noise, rather than simple additive noise. The assumption of signal-dependence complicates the problem considerably for non-robust estimators; for robust estimators, the problems are such that analytic solutions become impossible, and numerical methods must be used to derive the estimators. Both point-estimators and multi-parameter estimators are considered. In addition to the description of the various robust estimators, a comparison of their performance on real images corrupted by simulated signal-dependent noise with various (Gaussian and non-Gaussian) distributions is also presented.

Original language	American English
Journal	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	3
DOIs	https://doi.org/10.1109/ICASSP.1984.1172811
State	Published - Mar 1984

Keywords

Noise robustness
Additive noise
Statistics
Image restoration
Signal restoration
Gaussian noise
Stochastic resonance
Pixel
Random variables

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title = "ROBUST IMAGE ESTIMATION IN SIGNAL-DEPENDENT NOISE.",

abstract = "Several estimators for the digital restoration of a noisy image are presented. These estimators differ from those previously found in the literature in that they are robust for deviations from the assumed signal or noise statistics, and the image is assumed to have been corrupted by signal-dependent or nonlinear noise, rather than simple additive noise. The assumption of signal-dependence complicates the problem considerably for non-robust estimators; for robust estimators, the problems are such that analytic solutions become impossible, and numerical methods must be used to derive the estimators. Both point-estimators and multi-parameter estimators are considered. In addition to the description of the various robust estimators, a comparison of their performance on real images corrupted by simulated signal-dependent noise with various (Gaussian and non-Gaussian) distributions is also presented.",

keywords = "Noise robustness, Additive noise, Statistics, Image restoration, Signal restoration, Gaussian noise, Stochastic resonance, Pixel, Random variables",

author = "Sin-Horng Chen and John Murray and Walkup, {John F.}",

year = "1984",

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doi = "10.1109/ICASSP.1984.1172811",

language = "American English",

volume = "3",

journal = "Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing",

issn = "0736-7791",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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T1 - ROBUST IMAGE ESTIMATION IN SIGNAL-DEPENDENT NOISE.

AU - Chen, Sin-Horng

AU - Murray, John

AU - Walkup, John F.

PY - 1984/3

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N2 - Several estimators for the digital restoration of a noisy image are presented. These estimators differ from those previously found in the literature in that they are robust for deviations from the assumed signal or noise statistics, and the image is assumed to have been corrupted by signal-dependent or nonlinear noise, rather than simple additive noise. The assumption of signal-dependence complicates the problem considerably for non-robust estimators; for robust estimators, the problems are such that analytic solutions become impossible, and numerical methods must be used to derive the estimators. Both point-estimators and multi-parameter estimators are considered. In addition to the description of the various robust estimators, a comparison of their performance on real images corrupted by simulated signal-dependent noise with various (Gaussian and non-Gaussian) distributions is also presented.

AB - Several estimators for the digital restoration of a noisy image are presented. These estimators differ from those previously found in the literature in that they are robust for deviations from the assumed signal or noise statistics, and the image is assumed to have been corrupted by signal-dependent or nonlinear noise, rather than simple additive noise. The assumption of signal-dependence complicates the problem considerably for non-robust estimators; for robust estimators, the problems are such that analytic solutions become impossible, and numerical methods must be used to derive the estimators. Both point-estimators and multi-parameter estimators are considered. In addition to the description of the various robust estimators, a comparison of their performance on real images corrupted by simulated signal-dependent noise with various (Gaussian and non-Gaussian) distributions is also presented.

KW - Noise robustness

KW - Additive noise

KW - Statistics

KW - Image restoration

KW - Signal restoration

KW - Gaussian noise

KW - Stochastic resonance

KW - Pixel

KW - Random variables

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DO - 10.1109/ICASSP.1984.1172811

M3 - Conference article

AN - SCOPUS:0021181079

VL - 3

JO - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing

JF - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing

SN - 0736-7791

ER -

ROBUST IMAGE ESTIMATION IN SIGNAL-DEPENDENT NOISE.

Abstract

Keywords

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