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: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › 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.

Original language	English
Title of host publication	Proceedings of the 2014 IEEE International Conference on Image Processing, ICIP 2014
Publisher	IEEE
Pages	813-817
Number of pages	5
ISBN (Electronic)	9781479957514
DOIs	https://doi.org/10.1109/ICIP.2014.7025163
Publication status	Published - 2014
Externally published	Yes
Event	2014 IEEE International Conference on Image Processing (ICIP) - CNIT La Défense, Paris, France Duration: 27 Oct 2014 → 30 Oct 2014 https://icip2014.wp.imt.fr/

Conference

Conference	2014 IEEE International Conference on Image Processing (ICIP)
Country/Territory	France
City	Paris
Period	27/10/14 → 30/10/14
Internet address	https://icip2014.wp.imt.fr/

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

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

Access to Document

10.1109/ICIP.2014.7025163

Cite this

@inproceedings{93776d9142774427844f9f1c31a4b01d,

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.",

keywords = "Dual Denoising, Fourier shrinkage, Image denoising, Non-Local Bayes, Patch-Based methods",

author = "N. PIERAZZO and M. LEBRUN and RAIS, \{M. E.\} and MOREL, \{J. M.\} and G. FACCIOLO",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE International Conference on Image Processing (ICIP) ; Conference date: 27-10-2014 Through 30-10-2014",

year = "2014",

doi = "10.1109/ICIP.2014.7025163",

language = "English",

pages = "813--817",

booktitle = "Proceedings of the 2014 IEEE International Conference on Image Processing, ICIP 2014",

publisher = "IEEE",

url = "https://icip2014.wp.imt.fr/",

}

TY - GEN

T1 - Non-local dual image denoising

AU - PIERAZZO, N.

AU - LEBRUN, M.

AU - RAIS, M. E.

AU - MOREL, J. M.

AU - FACCIOLO, G.

PY - 2014

Y1 - 2014

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.

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.

KW - Dual Denoising

KW - Fourier shrinkage

KW - Image denoising

KW - Non-Local Bayes

KW - Patch-Based methods

UR - https://www.scopus.com/pages/publications/84949927720

U2 - 10.1109/ICIP.2014.7025163

DO - 10.1109/ICIP.2014.7025163

M3 - Conference paper (refereed)

AN - SCOPUS:84949927720

SP - 813

EP - 817

BT - Proceedings of the 2014 IEEE International Conference on Image Processing, ICIP 2014

PB - IEEE

T2 - 2014 IEEE International Conference on Image Processing (ICIP)

Y2 - 27 October 2014 through 30 October 2014

ER -

Non-local dual image denoising

Abstract

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this