First order roughness penalty for hyperspectral image denoising

Behnood Rasti; Johannes R. Sveinsson; Magnus O. Ulfarsson; Jakob Sigurdsson

doi:10.1109/WHISPERS.2013.8080623

First order roughness penalty for hyperspectral image denoising

Behnood Rasti, Johannes R. Sveinsson, Magnus O. Ulfarsson, Jakob Sigurdsson

Faculty of Electrical and Computer Engineering

University of Iceland

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper, a new denoising method for hyperspectral images using First Order Roughness Penalty (FORP) is proposed. The proposed algorithm is applied in the wavelet domain to exploit the multiresolution analysis property of wavelets and thus improving the denoising results. Stein's Unbiased Risk Estimator (SURE) is used to choose the tuning parameters automatically. The experimental results show improvements for simulated data sets based on Signal to Noise Ratio (SNR) and visually for real data set.

Original language	English
Title of host publication	2013 5th Workshop on Hyperspectral Image and Signal Processing
Subtitle of host publication	Evolution in Remote Sensing, WHISPERS 2013
Publisher	IEEE Computer Society
ISBN (Electronic)	9781509011193
DOIs	https://doi.org/10.1109/WHISPERS.2013.8080623
Publication status	Published - 28 Jun 2013
Event	5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2013 - Gainesville, United States Duration: 26 Jun 2013 → 28 Jun 2013

Publication series

Name	Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volume	2013-June

Conference

Conference	5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2013
Country/Territory	United States
City	Gainesville
Period	26/06/13 → 28/06/13

Bibliographical note

Funding Information: This work was supported by the Doctoral Grants of the University of Iceland Research Fund and the University of Iceland Research Fund, and the Icelandic research fund (130635-051). Publisher Copyright: © 2013 IEEE.

Other keywords

Denoising
First order roughness penalty
Hyperspectral image
Multiresolution analysis
Stein's unbiased risk estimator
Wavelets

Access to Document

10.1109/WHISPERS.2013.8080623

Cite this

Rasti, B., Sveinsson, J. R., Ulfarsson, M. O., & Sigurdsson, J. (2013). First order roughness penalty for hyperspectral image denoising. In 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2013 Article 8080623 (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing; Vol. 2013-June). IEEE Computer Society. https://doi.org/10.1109/WHISPERS.2013.8080623

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title = "First order roughness penalty for hyperspectral image denoising",

abstract = "In this paper, a new denoising method for hyperspectral images using First Order Roughness Penalty (FORP) is proposed. The proposed algorithm is applied in the wavelet domain to exploit the multiresolution analysis property of wavelets and thus improving the denoising results. Stein's Unbiased Risk Estimator (SURE) is used to choose the tuning parameters automatically. The experimental results show improvements for simulated data sets based on Signal to Noise Ratio (SNR) and visually for real data set.",

keywords = "Denoising, First order roughness penalty, Hyperspectral image, Multiresolution analysis, Stein's unbiased risk estimator, Wavelets",

author = "Behnood Rasti and Sveinsson, \{Johannes R.\} and Ulfarsson, \{Magnus O.\} and Jakob Sigurdsson",

note = "Funding Information: This work was supported by the Doctoral Grants of the University of Iceland Research Fund and the University of Iceland Research Fund, and the Icelandic research fund (130635-051). Publisher Copyright: {\textcopyright} 2013 IEEE.; 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2013 ; Conference date: 26-06-2013 Through 28-06-2013",

year = "2013",

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day = "28",

doi = "10.1109/WHISPERS.2013.8080623",

language = "English",

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Rasti, B, Sveinsson, JR, Ulfarsson, MO & Sigurdsson, J 2013, First order roughness penalty for hyperspectral image denoising. in 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2013., 8080623, Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, vol. 2013-June, IEEE Computer Society, 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2013, Gainesville, United States, 26/06/13. https://doi.org/10.1109/WHISPERS.2013.8080623

First order roughness penalty for hyperspectral image denoising. / Rasti, Behnood; Sveinsson, Johannes R.; Ulfarsson, Magnus O. et al.
2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2013. IEEE Computer Society, 2013. 8080623 (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing; Vol. 2013-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - First order roughness penalty for hyperspectral image denoising

AU - Rasti, Behnood

AU - Sveinsson, Johannes R.

AU - Ulfarsson, Magnus O.

AU - Sigurdsson, Jakob

N1 - Funding Information: This work was supported by the Doctoral Grants of the University of Iceland Research Fund and the University of Iceland Research Fund, and the Icelandic research fund (130635-051). Publisher Copyright: © 2013 IEEE.

PY - 2013/6/28

Y1 - 2013/6/28

N2 - In this paper, a new denoising method for hyperspectral images using First Order Roughness Penalty (FORP) is proposed. The proposed algorithm is applied in the wavelet domain to exploit the multiresolution analysis property of wavelets and thus improving the denoising results. Stein's Unbiased Risk Estimator (SURE) is used to choose the tuning parameters automatically. The experimental results show improvements for simulated data sets based on Signal to Noise Ratio (SNR) and visually for real data set.

AB - In this paper, a new denoising method for hyperspectral images using First Order Roughness Penalty (FORP) is proposed. The proposed algorithm is applied in the wavelet domain to exploit the multiresolution analysis property of wavelets and thus improving the denoising results. Stein's Unbiased Risk Estimator (SURE) is used to choose the tuning parameters automatically. The experimental results show improvements for simulated data sets based on Signal to Noise Ratio (SNR) and visually for real data set.

KW - Denoising

KW - First order roughness penalty

KW - Hyperspectral image

KW - Multiresolution analysis

KW - Stein's unbiased risk estimator

KW - Wavelets

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DO - 10.1109/WHISPERS.2013.8080623

M3 - Conference contribution

T3 - Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing

BT - 2013 5th Workshop on Hyperspectral Image and Signal Processing

PB - IEEE Computer Society

T2 - 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2013

Y2 - 26 June 2013 through 28 June 2013

ER -

Rasti B, Sveinsson JR, Ulfarsson MO , Sigurdsson J. First order roughness penalty for hyperspectral image denoising. In 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2013. IEEE Computer Society. 2013. 8080623. (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing). doi: 10.1109/WHISPERS.2013.8080623

First order roughness penalty for hyperspectral image denoising

Abstract

Publication series

Conference

Bibliographical note

Other keywords

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