Time-of-flight depth image denoising using prior noise information

T. Edeler; K. Ohliger; S. Hussmann; A. Mertins

doi:10.1109/ICOSP.2010.5656819

Time-of-flight depth image denoising using prior noise information

T. Edeler, K. Ohliger, S. Hussmann, A. Mertins

Institute of Signal Processing

Abstract

In this paper, we propose a novel way of using time-of-flight camera depth and amplitude images to reduce the noise in depth images with prior knowledge of spatial noise distribution, which is correlated with the incident light falling on each pixel. The denoising is done in wavelet space and the influence and implications of the extended noise model to wavelet space and common denoising methods are shown.

Original language	English
Title of host publication	IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS
Number of pages	4
Place of Publication	Beijing, China
Publisher	IEEE
Publication date	01.10.2010
Pages	119-122
Article number	5656819
ISBN (Print)	978-1-4244-5897-4
ISBN (Electronic)	978-1-4244-5900-1
DOIs	https://doi.org/10.1109/ICOSP.2010.5656819
Publication status	Published - 01.10.2010
Event	2010 IEEE 10th International Conference on Signal Processing - Beijing, China Duration: 24.10.2010 → 28.10.2010 Conference number: 83255

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Cite this

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title = "Time-of-flight depth image denoising using prior noise information",

abstract = "In this paper, we propose a novel way of using time-of-flight camera depth and amplitude images to reduce the noise in depth images with prior knowledge of spatial noise distribution, which is correlated with the incident light falling on each pixel. The denoising is done in wavelet space and the influence and implications of the extended noise model to wavelet space and common denoising methods are shown.",

author = "T. Edeler and K. Ohliger and S. Hussmann and A. Mertins",

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Edeler, T, Ohliger, K, Hussmann, S & Mertins, A 2010, Time-of-flight depth image denoising using prior noise information. in IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS., 5656819, International Conference on Signal Processing Proceedings, ICSP, IEEE, Beijing, China, pp. 119-122, 2010 IEEE 10th International Conference on Signal Processing, Beijing, China, 24.10.10. https://doi.org/10.1109/ICOSP.2010.5656819

Time-of-flight depth image denoising using prior noise information. / Edeler, T.; Ohliger, K.; Hussmann, S. et al.
IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS. Beijing, China: IEEE, 2010. p. 119-122 5656819 (International Conference on Signal Processing Proceedings, ICSP).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

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T1 - Time-of-flight depth image denoising using prior noise information

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AU - Ohliger, K.

AU - Hussmann, S.

AU - Mertins, A.

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PY - 2010/10/1

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N2 - In this paper, we propose a novel way of using time-of-flight camera depth and amplitude images to reduce the noise in depth images with prior knowledge of spatial noise distribution, which is correlated with the incident light falling on each pixel. The denoising is done in wavelet space and the influence and implications of the extended noise model to wavelet space and common denoising methods are shown.

AB - In this paper, we propose a novel way of using time-of-flight camera depth and amplitude images to reduce the noise in depth images with prior knowledge of spatial noise distribution, which is correlated with the incident light falling on each pixel. The denoising is done in wavelet space and the influence and implications of the extended noise model to wavelet space and common denoising methods are shown.

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DO - 10.1109/ICOSP.2010.5656819

M3 - Conference contribution

SN - 978-1-4244-5897-4

T3 - International Conference on Signal Processing Proceedings, ICSP

SP - 119

EP - 122

BT - IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS

PB - IEEE

CY - Beijing, China

T2 - 2010 IEEE 10th International Conference on Signal Processing

Y2 - 24 October 2010 through 28 October 2010

ER -

Time-of-flight depth image denoising using prior noise information

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