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BM3D image denoising using heterogeneous computing platforms

Tutkimustuotosvertaisarvioitu

Standard

BM3D image denoising using heterogeneous computing platforms. / Sarjanoja, Sampsa; Boutellier, Jani; Hannuksela, Jari.

DASIP 2015 - Proceedings of the 2015 Conference on Design and Architectures for Signal and Image Processing. Vuosikerta 2015-December IEEE COMPUTER SOCIETY PRESS, 2015. 7367257.

Tutkimustuotosvertaisarvioitu

Harvard

Sarjanoja, S, Boutellier, J & Hannuksela, J 2015, BM3D image denoising using heterogeneous computing platforms. julkaisussa DASIP 2015 - Proceedings of the 2015 Conference on Design and Architectures for Signal and Image Processing. Vuosikerta. 2015-December, 7367257, IEEE COMPUTER SOCIETY PRESS, Cracow, Puola, 23/09/15. https://doi.org/10.1109/DASIP.2015.7367257

APA

Sarjanoja, S., Boutellier, J., & Hannuksela, J. (2015). BM3D image denoising using heterogeneous computing platforms. teoksessa DASIP 2015 - Proceedings of the 2015 Conference on Design and Architectures for Signal and Image Processing (Vuosikerta 2015-December). [7367257] IEEE COMPUTER SOCIETY PRESS. https://doi.org/10.1109/DASIP.2015.7367257

Vancouver

Sarjanoja S, Boutellier J, Hannuksela J. BM3D image denoising using heterogeneous computing platforms. julkaisussa DASIP 2015 - Proceedings of the 2015 Conference on Design and Architectures for Signal and Image Processing. Vuosikerta 2015-December. IEEE COMPUTER SOCIETY PRESS. 2015. 7367257 https://doi.org/10.1109/DASIP.2015.7367257

Author

Sarjanoja, Sampsa ; Boutellier, Jani ; Hannuksela, Jari. / BM3D image denoising using heterogeneous computing platforms. DASIP 2015 - Proceedings of the 2015 Conference on Design and Architectures for Signal and Image Processing. Vuosikerta 2015-December IEEE COMPUTER SOCIETY PRESS, 2015.

Bibtex - Lataa

@inproceedings{4587212fe1d04c74915033449e9ab51c,
title = "BM3D image denoising using heterogeneous computing platforms",
abstract = "Noise reduction is often performed at an early stage of the image processing path. In order to keep the processing delays small in different computing platforms, it is important that the noise reduction is performed swiftly. In this paper, the block-matching and three-dimensional filtering (BM3D) denoising algorithm is implemented on heterogeneous computing platforms using OpenCL and CUDA frameworks. To our knowledge, these implementations are the first successful open source attempts to use GPU computation for BM3D denoising. The presented GPU implementations are up to 7.5 times faster than their respective CPU implementations. At the same time, the experiments illustrate general design challenges in using massively parallel processing platforms for the calculation of complex imaging algorithms.",
keywords = "Image denoising, Mobile computing, Parallel algorithms, Parallel processing",
author = "Sampsa Sarjanoja and Jani Boutellier and Jari Hannuksela",
year = "2015",
month = "12",
day = "28",
doi = "10.1109/DASIP.2015.7367257",
language = "English",
volume = "2015-December",
booktitle = "DASIP 2015 - Proceedings of the 2015 Conference on Design and Architectures for Signal and Image Processing",
publisher = "IEEE COMPUTER SOCIETY PRESS",

}

RIS (suitable for import to EndNote) - Lataa

TY - GEN

T1 - BM3D image denoising using heterogeneous computing platforms

AU - Sarjanoja, Sampsa

AU - Boutellier, Jani

AU - Hannuksela, Jari

PY - 2015/12/28

Y1 - 2015/12/28

N2 - Noise reduction is often performed at an early stage of the image processing path. In order to keep the processing delays small in different computing platforms, it is important that the noise reduction is performed swiftly. In this paper, the block-matching and three-dimensional filtering (BM3D) denoising algorithm is implemented on heterogeneous computing platforms using OpenCL and CUDA frameworks. To our knowledge, these implementations are the first successful open source attempts to use GPU computation for BM3D denoising. The presented GPU implementations are up to 7.5 times faster than their respective CPU implementations. At the same time, the experiments illustrate general design challenges in using massively parallel processing platforms for the calculation of complex imaging algorithms.

AB - Noise reduction is often performed at an early stage of the image processing path. In order to keep the processing delays small in different computing platforms, it is important that the noise reduction is performed swiftly. In this paper, the block-matching and three-dimensional filtering (BM3D) denoising algorithm is implemented on heterogeneous computing platforms using OpenCL and CUDA frameworks. To our knowledge, these implementations are the first successful open source attempts to use GPU computation for BM3D denoising. The presented GPU implementations are up to 7.5 times faster than their respective CPU implementations. At the same time, the experiments illustrate general design challenges in using massively parallel processing platforms for the calculation of complex imaging algorithms.

KW - Image denoising

KW - Mobile computing

KW - Parallel algorithms

KW - Parallel processing

UR - http://www.scopus.com/inward/record.url?scp=84959887479&partnerID=8YFLogxK

U2 - 10.1109/DASIP.2015.7367257

DO - 10.1109/DASIP.2015.7367257

M3 - Conference contribution

VL - 2015-December

BT - DASIP 2015 - Proceedings of the 2015 Conference on Design and Architectures for Signal and Image Processing

PB - IEEE COMPUTER SOCIETY PRESS

ER -