Optimizing the data adaptive dual domain denoising algorithm

Nicola PIERAZZO; Jean-Michel MOREL; Gabriele FACCIOLO

doi:10.1007/978-3-319-25751-8_43

Optimizing the data adaptive dual domain denoising algorithm

Nicola PIERAZZO^*
, Jean-Michel MOREL
, Gabriele FACCIOLO

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

1 Citation (Scopus)

Abstract

This paper presents two new strategies that greatly improve the execution time of the DA3D Algorithm, a new denoising algorithm with state-of-the-art results. First, the weight map used in DA3D is implemented as a quad-tree. This greatly reduces the time needed to search the minimum weight, greatly reducing the overall computation time. Second, a simple but effective tiling strategy is shown to work in order to allow the parallel execution of the algorithm. This allows the implementation of DA3D in a parallel architecture. Both these improvements do not affect the quality of the output.

Original language	English
Title of host publication	Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 20th Iberoamerican Congress, CIARP 2015, Proceedings
Editors	Alvaro PARDO, Josef KITTLER
Publisher	Springer, Cham
Pages	358-365
Number of pages	8
ISBN (Electronic)	9783319257518
ISBN (Print)	9783319257501
DOIs	https://doi.org/10.1007/978-3-319-25751-8_43
Publication status	Published - 2015
Externally published	Yes
Event	20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015 - Montevideo, Uruguay Duration: 9 Nov 2015 → 12 Nov 2015

Publication series

Name	Lecture Notes in Computer Science
Volume	9423
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015
Country/Territory	Uruguay
City	Montevideo
Period	9/11/15 → 12/11/15

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

Funding

Work partly founded by Centre National d’Etudes Spatiales (MISS Project), European Research Council (advanced grant Twelve Labours), Office of Naval research (ONR grant N00014-14-1-0023), DGA Stéréo project, ANR-DGA (project ANR-12-ASTR-0035), FUI (project Plein Phare) and Institut Universitaire de France.

Keywords

Image denoising
Parallel processing
Quad-tree

Access to Document

10.1007/978-3-319-25751-8_43

Cite this

PIERAZZO, N., MOREL, J.-M., & FACCIOLO, G. (2015). Optimizing the data adaptive dual domain denoising algorithm. In A. PARDO, & J. KITTLER (Eds.), Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 20th Iberoamerican Congress, CIARP 2015, Proceedings (pp. 358-365). (Lecture Notes in Computer Science; Vol. 9423). Springer, Cham. https://doi.org/10.1007/978-3-319-25751-8_43

PIERAZZO, Nicola ; MOREL, Jean-Michel ; FACCIOLO, Gabriele. / Optimizing the data adaptive dual domain denoising algorithm. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 20th Iberoamerican Congress, CIARP 2015, Proceedings. editor / Alvaro PARDO ; Josef KITTLER. Springer, Cham, 2015. pp. 358-365 (Lecture Notes in Computer Science).

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title = "Optimizing the data adaptive dual domain denoising algorithm",

abstract = "This paper presents two new strategies that greatly improve the execution time of the DA3D Algorithm, a new denoising algorithm with state-of-the-art results. First, the weight map used in DA3D is implemented as a quad-tree. This greatly reduces the time needed to search the minimum weight, greatly reducing the overall computation time. Second, a simple but effective tiling strategy is shown to work in order to allow the parallel execution of the algorithm. This allows the implementation of DA3D in a parallel architecture. Both these improvements do not affect the quality of the output.",

keywords = "Image denoising, Parallel processing, Quad-tree",

author = "Nicola PIERAZZO and Jean-Michel MOREL and Gabriele FACCIOLO",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015 ; Conference date: 09-11-2015 Through 12-11-2015",

year = "2015",

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PIERAZZO, N, MOREL, J-M & FACCIOLO, G 2015, Optimizing the data adaptive dual domain denoising algorithm. in A PARDO & J KITTLER (eds), Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 20th Iberoamerican Congress, CIARP 2015, Proceedings. Lecture Notes in Computer Science, vol. 9423, Springer, Cham, pp. 358-365, 20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015, Montevideo, Uruguay, 9/11/15. https://doi.org/10.1007/978-3-319-25751-8_43

Optimizing the data adaptive dual domain denoising algorithm. / PIERAZZO, Nicola; MOREL, Jean-Michel; FACCIOLO, Gabriele.
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 20th Iberoamerican Congress, CIARP 2015, Proceedings. ed. / Alvaro PARDO; Josef KITTLER. Springer, Cham, 2015. p. 358-365 (Lecture Notes in Computer Science; Vol. 9423).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

TY - GEN

T1 - Optimizing the data adaptive dual domain denoising algorithm

AU - PIERAZZO, Nicola

AU - MOREL, Jean-Michel

AU - FACCIOLO, Gabriele

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - This paper presents two new strategies that greatly improve the execution time of the DA3D Algorithm, a new denoising algorithm with state-of-the-art results. First, the weight map used in DA3D is implemented as a quad-tree. This greatly reduces the time needed to search the minimum weight, greatly reducing the overall computation time. Second, a simple but effective tiling strategy is shown to work in order to allow the parallel execution of the algorithm. This allows the implementation of DA3D in a parallel architecture. Both these improvements do not affect the quality of the output.

AB - This paper presents two new strategies that greatly improve the execution time of the DA3D Algorithm, a new denoising algorithm with state-of-the-art results. First, the weight map used in DA3D is implemented as a quad-tree. This greatly reduces the time needed to search the minimum weight, greatly reducing the overall computation time. Second, a simple but effective tiling strategy is shown to work in order to allow the parallel execution of the algorithm. This allows the implementation of DA3D in a parallel architecture. Both these improvements do not affect the quality of the output.

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KW - Parallel processing

KW - Quad-tree

UR - https://www.scopus.com/pages/publications/84983646528

U2 - 10.1007/978-3-319-25751-8_43

DO - 10.1007/978-3-319-25751-8_43

M3 - Conference paper (refereed)

AN - SCOPUS:84983646528

SN - 9783319257501

T3 - Lecture Notes in Computer Science

SP - 358

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BT - Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 20th Iberoamerican Congress, CIARP 2015, Proceedings

A2 - PARDO, Alvaro

A2 - KITTLER, Josef

PB - Springer, Cham

T2 - 20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015

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PIERAZZO N, MOREL JM, FACCIOLO G. Optimizing the data adaptive dual domain denoising algorithm. In PARDO A, KITTLER J, editors, Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 20th Iberoamerican Congress, CIARP 2015, Proceedings. Springer, Cham. 2015. p. 358-365. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-25751-8_43

Optimizing the data adaptive dual domain denoising algorithm

Abstract

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Keywords

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