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Two Parallel Strategies for Real-time Spatial Video Denoising for Multi-core Processors

by Banpot Dolwithayaku, Chantana Chantrapornchai, Noppadol Chumchob
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 48 - Number 16
Year of Publication: 2012
Authors: Banpot Dolwithayaku, Chantana Chantrapornchai, Noppadol Chumchob
10.5120/7433-0397

Banpot Dolwithayaku, Chantana Chantrapornchai, Noppadol Chumchob . Two Parallel Strategies for Real-time Spatial Video Denoising for Multi-core Processors. International Journal of Computer Applications. 48, 16 ( June 2012), 28-35. DOI=10.5120/7433-0397

@article{ 10.5120/7433-0397,
author = { Banpot Dolwithayaku, Chantana Chantrapornchai, Noppadol Chumchob },
title = { Two Parallel Strategies for Real-time Spatial Video Denoising for Multi-core Processors },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 48 },
number = { 16 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 28-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume48/number16/7433-0397/ },
doi = { 10.5120/7433-0397 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:44:14.338319+05:30
%A Banpot Dolwithayaku
%A Chantana Chantrapornchai
%A Noppadol Chumchob
%T Two Parallel Strategies for Real-time Spatial Video Denoising for Multi-core Processors
%J International Journal of Computer Applications
%@ 0975-8887
%V 48
%N 16
%P 28-35
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Video denoising is usually a time consuming process especially for large video files. With the advancement of the processor technology, it is possible to perform video denoising in real-time on multi-core processors. In this paper, we study parallel techniques for denoising real-time video on multi-core processor which work on both shared memory model and distributed memory model. We investigate two approaches: a block approach, which assigns a group of threads to each block of video frames; and a distributor approach, which uses one thread to distribute the frame data to each thread. Our experiments focus on the image denoising technique based on the total variation but the approach can be integrated with other image denoising algorithm like discrete wavelet transform (DWT) or diffusion technique. We found that by using the distributor strategy, we can achieve speedup which is 1. 27 times faster than the block strategy and the video frame rate can be increased by 7. 43%. Moreover, we also apply the prefetching technique which further enhance frame rate by 22. 02% and frame rate control to stabilize frame rate and retain the original video length during denoising and playing in real-time. Our method also has good denoised quality which is better than previous work in [1] in average case.

References
  1. A. Sarhan, M. T. Faheem, R. O. Mahmoud, "A Proposed Intelligent Denoising Technique for Spatial Video Denoising for Real-Time Applications", Intl J. Mobile Comp and Mobile Comm (IJMCMC), vol. 2 2010.
  2. L. I. Rudin, S. Osher, E. Fatami, "Nonlinear total variation based noise removal algorithms", Physica D. , vol. 60, pp. 259—268, 1992.
  3. C. R. Vogel, M. E. Oman, "Fast, robust total variation-based reconstruction of noisy, blurred images", IEEE Trans. Image Process, vol. 7, pp. 813—824, 1998.
  4. C. R. Vogel, M. E. Oman, "Iterative Methods for Total Variation Denoising" , SIAM J. Sci. Comput. , vol. 17, pp. 227—238, 1996.
  5. B. Dolwithayakul, C. Chantrapornchai, N. Chumchob, "GPU-Based Total Variation Image Restoration using Sliding Window Gauss-Seidel Algorithm", Proceeding of Intelligent Signal Processing and Communication Systems (ISPACS 2011), pp. 1—6 , 2011.
  6. Paul R. , M. Meyer, "Restoration of motion picture film," Conservation and Museology, Butterworth-Heinemann, 2000.
  7. T. F. Chan, G. H. Gohub, P. Mulet, "A nonlinear primal-dual method for total variation based image restoration", SIAM J. Sci. Comput. , vol. 20, pp. 1964—1977, 1999.
  8. R. Bagnara, "A unified proof for the convergence of Jacobi and Gauss-Seidel methods. ", J. SIAM Review, vol. 37(1), pp. 93—97, 1995.
  9. A. Ogier, P. Hellier, C. Barillot, "Restoration of 3D medical image with total variation scheme on wavelet domain (TVW)", Proceeding of the SPIE, vol. 6144, pp. 465—473, 2006.
  10. P. Piastowski, "Image processing to reduce blocking artifacts," US Patent Application US20060274959, 2006.
  11. Xiph. org, "Xiph. org Test Media", Available via http://media. xiph. org/video/derf/, Accessed 14 May 2012.
  12. ITU-T, "H. 262 Information technology – Generic coding of moving pictures and associated audio information: Video", International Telecommunication Union-Telecommunication Standardization Sector, 2000.
  13. C. Dolar, M. M. Richter, H. Schroder, "Total variation regularization filtering for video signal processing", Proceeding of 13th IEEE International Symposium on Consumer Electronics(ISCE2009), pp. 1—5, 2009.
  14. Chiariglione. org, "MPEG standards – Full list of standards developed or under development", MPEG, Retrieved 31 Oct. 2009
  15. J. Ive, "Image formats for HDTV", European Broadcasting Union Technical Review, 2004.
  16. NVIDIA® Corporation, "NVIDIA CUDA compute unified device architecture programming guide version 2. 1", 2008.
  17. A. Munshi, "OpenCL Parallel Computing on the GPU and CPU", International Conference and Exhibition on Computer Graphics and Interactive Technique (SIGGRAPH 2008), 2008
  18. A. Marquina, S. Osher, "Explicit algorithm for a new time dependent model based on level set motion for nonlinear deblurring and noise removal", SIAM J. Sci. Compute. , vol. 22, pp. 387—405, 2000.
  19. J. Gu. , Y. Sun, "Optimizing a parallel video encoder with message passing and a shared memory architecture", Tsinghua Science and Technology, vol. 16(4), pp. 393—398, 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Video Denoising Parallel Computing Openmp Rof Model Total Variation

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