Advertisement

Implementation of BM3D Filter on Intel Xeon Phi for Rendering in Blender Cycles

  • Milan Jaros
  • Petr StrakosEmail author
  • Tomas Karasek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11087)

Abstract

In this paper parallel implementation of Sparse 3D Transform-Domain Collaborative filter (BM3D) on the Intel Xeon Phi architecture is presented. Efficiency of the implementation in terms of speedup compared to serial implementation of the filter is demonstrated on denoising of rendered images. We also provide comparison with another parallel CPU version and show that ours performs better.

Using the state-of-the-art image filters such as BM3D offers powerful denoising capability in the area of image filtering. To achieve the highest possible quality of the result, the filter has to perform multiple demanding tasks over a single image. Effective implementation of the filter is therefore very important. This is also the case, when filtering is used for image rendering. Rendering times can be significantly decreased by application of powerful time efficient denoising filters. Unfortunately the existing serial implementation of the BM3D filter is time consuming. In this paper we provide efficient parallel implementation of the BM3D filter, and we apply it as a noise reduction technique to the rendered images that reduces the rendering times. We also provide an optimized version of the filter for the Intel Xeon Phi and Intel Xeon architecture.

Keywords

Image denoising Intel Xeon Phi Blender cycles Rendering Collaborative filtering High performance computing 

Notes

Acknowledgements

This work was supported by The Ministry of Education, Youth and Sports from the Large Infrastructures for Research, Experimental Development and Innovations project “IT4Innovations National Supercomputing Center - LM2015070”.

References

  1. 1.
    Bauszat, P., Eisemann, M., Magnor, M.: Guided image filtering for interactive high-quality global illumination. Comput. Graph. Forum 30(4), 1361–1368 (2011)CrossRefGoogle Scholar
  2. 2.
    Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Color image denoising via sparse 3D collaborative filtering with grouping constraint in luminance-chrominance space. In: Proceedings - International Conference on Image Processing, ICIP, vol. 1, pp. I313–I316 (2006)Google Scholar
  3. 3.
    Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising with block-matching and 3D filtering. In: Proceedings of SPIE - The International Society for Optical Engineering, vol. 6064 (2006)Google Scholar
  4. 4.
    Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Dammertz, H., Sewtz, D., Hanika, J., Lensch, H.P.A.: Edge-avoiding a-trous wavelet transform for fast global illumination filtering. In: Doggett, M., Laine, S., Hunt, W. (eds.) High Performance Graphics. The Eurographics Association (2010)Google Scholar
  6. 6.
    Kalantari, N.K., Sen, P.: Removing the noise in Monte Carlo rendering with general image denoising algorithms. Comput. Graph. Forum 32(2 Part 1), 93–102 (2013)CrossRefGoogle Scholar
  7. 7.
    Katkovnik, V., Foi, A., Egiazarian, K., Astola, J.: From local kernel to nonlocal multiple-model image denoising. Int. J. Comput. Vis. 86(1), 1–32 (2010)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Lebrun, M.: An analysis and implementation of the BM3D image denoising method. Image Process. On Line 2, 175–213 (2012)CrossRefGoogle Scholar
  9. 9.
    Pharr, M., Jakob, W., Humphreys, G.: Physically Based Rendering: From Theory to Implementation, 3rd edn, pp. 1–1233 (2016)Google Scholar
  10. 10.
    Rousselle, F., Knaus, C., Zwicker, M.: Adaptive sampling and reconstruction using greedy error minimization. ACM Trans. Graph. 30(6), 159:1–159:12 (2011)CrossRefGoogle Scholar
  11. 11.
    Sarjanoja, S., Boutellier, J., Hannuksela, J.: BM3D image denoising using heterogeneous computing platforms. In: Conference on Design and Architectures for Signal and Image Processing, DASIP, vol. 2015, December 2015Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.IT4Innovations, VSB - Technical University of OstravaOstrava-PorubaCzech Republic

Personalised recommendations