Non-local dual image denoising

N. Pierazzo; M. Lebrun; M. E. Rais; J. M. Morel; G. Facciolo

doi:10.1109/ICIP.2014.7025163

Non-local dual image denoising

N. Pierazzo
, M. Lebrun
, M. E. Rais
, J. M. Morel
, G. Facciolo

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

33 Citations (Scopus)

Abstract

The current state-of-the-art non-local algorithms for image denoising have the tendency to remove many low contrast details. Frequency-based algorithms keep these details, but on the other hand many artifacts are introduced. Recently, the Dual Domain Image Denoising (DDID) method has been proposed to address this issue. While beating the state-of-the-art, this algorithm still causes strong frequency domain artifacts. This paper reviews DDID under a different light, allowing to understand their origin. The analysis leads to the development of NLDD, a new denoising algorithm that outperforms DDID, BM3D and other state-of-the-art algorithms. NLDD is also three times faster than DDID and easily parallelizable. © 2014 IEEE.

Original language	English
Title of host publication	2014 IEEE International Conference on Image Processing, ICIP 2014
Publisher	IEEE
Pages	813-817
ISBN (Print)	9781479957514
DOIs	https://doi.org/10.1109/ICIP.2014.7025163
Publication status	Published - 28 Jan 2014
Externally published	Yes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

Aknowledgements: work partially supported by Centre National d’Etudes Spatiales (MISS Project), European Research Council (Advanced Grant Twelve Labours), Office of Naval Research (under Grant N00014- 97-1-0839), Direction Générale de l’Armement, Fondation Mathématique Jacques Hadamard and Agence Nationale de la Recherche (Stereo project).

Research Keywords

Dual Denoising
Fourier shrinkage
Image denoising
Non-Local Bayes
Patch-Based methods

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10.1109/ICIP.2014.7025163

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title = "Non-local dual image denoising",

abstract = "The current state-of-the-art non-local algorithms for image denoising have the tendency to remove many low contrast details. Frequency-based algorithms keep these details, but on the other hand many artifacts are introduced. Recently, the Dual Domain Image Denoising (DDID) method has been proposed to address this issue. While beating the state-of-the-art, this algorithm still causes strong frequency domain artifacts. This paper reviews DDID under a different light, allowing to understand their origin. The analysis leads to the development of NLDD, a new denoising algorithm that outperforms DDID, BM3D and other state-of-the-art algorithms. NLDD is also three times faster than DDID and easily parallelizable. {\textcopyright} 2014 IEEE.",

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note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected]. ",

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AU - Pierazzo, N.

AU - Lebrun, M.

AU - Rais, M. E.

AU - Morel, J. M.

AU - Facciolo, G.

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2014/1/28

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N2 - The current state-of-the-art non-local algorithms for image denoising have the tendency to remove many low contrast details. Frequency-based algorithms keep these details, but on the other hand many artifacts are introduced. Recently, the Dual Domain Image Denoising (DDID) method has been proposed to address this issue. While beating the state-of-the-art, this algorithm still causes strong frequency domain artifacts. This paper reviews DDID under a different light, allowing to understand their origin. The analysis leads to the development of NLDD, a new denoising algorithm that outperforms DDID, BM3D and other state-of-the-art algorithms. NLDD is also three times faster than DDID and easily parallelizable. © 2014 IEEE.

AB - The current state-of-the-art non-local algorithms for image denoising have the tendency to remove many low contrast details. Frequency-based algorithms keep these details, but on the other hand many artifacts are introduced. Recently, the Dual Domain Image Denoising (DDID) method has been proposed to address this issue. While beating the state-of-the-art, this algorithm still causes strong frequency domain artifacts. This paper reviews DDID under a different light, allowing to understand their origin. The analysis leads to the development of NLDD, a new denoising algorithm that outperforms DDID, BM3D and other state-of-the-art algorithms. NLDD is also three times faster than DDID and easily parallelizable. © 2014 IEEE.

KW - Dual Denoising

KW - Fourier shrinkage

KW - Image denoising

KW - Non-Local Bayes

KW - Patch-Based methods

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DO - 10.1109/ICIP.2014.7025163

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781479957514

SP - 813

EP - 817

BT - 2014 IEEE International Conference on Image Processing, ICIP 2014

PB - IEEE

ER -

Non-local dual image denoising

Abstract

Bibliographical note

Funding

Research Keywords

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