GPU acceleration of NL-means, BM3D and VBM3D

Abstract

Denoising is an essential part of any image- or video-processing pipeline. Unfortunately, due to time-processing constraints, many pipelines do not consider the use of modern denoisers. These algorithms have only CPU implementations or suboptimal GPU implementations. We propose a new efficient GPU implementation of NL-means and BM3D, and, to our knowledge, the first GPU implementation of the video-denoising algorithm VBM3D. The performance of these implementations enable their use in real-time scenarios.

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Notes

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    Available on http://mcolom.info/download/no_noise_images/no_noise_images.zip.

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    https://media.xiph.org/video/derf.

References

  1. 1.

    Ali, R.A., Hardie, R.C.: Recursive non-local means filter for video denoising. EURASIP JIVP 1, 29 (2017)

    Google Scholar 

  2. 2.

    AMD: AMD APP SDK OpenCLTM Optimization Guide (2015)

  3. 3.

    Arias, P., Facciolo, G., Morel, J.M.: A comparison of patch-based models in video denoising. In: IEEE IVMSP, pp. 1–5 (2018)

  4. 4.

    Arias, P., Morel, J.M.: Video denoising via empirical bayesian estimation of space-time patches. JMIV 60(1), 70–93 (2018)

    MathSciNet  Article  Google Scholar 

  5. 5.

    Arias, P., Morel, J.M.: Kalman filtering of patches for frame-recursive video denoising. In: IEEE CVPRW (2019)

  6. 6.

    Aubert, G., Aujol, J.F.: A variational approach to removing multiplicative noise. SIAM SIIMS 68(4), 925–946 (2008)

    MathSciNet  MATH  Google Scholar 

  7. 7.

    Aujol, J.F., Aubert, G., Blanc-Féraud, L., Chambolle, A.: Image decomposition application to sar images. In: Springer Scale-Space, pp. 297–312 (2003)

  8. 8.

    Boulanger, J., Kervrann, C., Bouthemy, P., Elbau, P., Sibarita, J.B., Salamero, J.: Patch-based nonlocal functional for denoising fluorescence microscopy image sequences. IEEE TMI 29(2), 442–454 (2009)

    Google Scholar 

  9. 9.

    Bradski, G.: The OpenCV Library. Dr. Dobb’s Journal of Software Tools (2000)

  10. 10.

    Briand, T., Davy, A.: Optimization of image B-spline interpolation for GPU architectures. IPOL 9, 183–204 (2019)

    MathSciNet  Article  Google Scholar 

  11. 11.

    Brox, T., Kleinschmidt, O., Cremers, D.: Efficient nonlocal means for denoising of textural patterns. IEEE TIP 17(7), 1083–1092 (2008)

    MathSciNet  Google Scholar 

  12. 12.

    Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. IEEE CVPR 2, 60–65 (2005)

    MATH  Google Scholar 

  13. 13.

    Buades, A., Coll, B., Morel, J.M.: Non-local means denoising. IPOL 1, 208–212 (2011)

    MATH  Google Scholar 

  14. 14.

    Buades, A., Lisani, J.L., Miladinović, M.: Patch-based video denoising with optical flow estimation. IEEE TIP 25(6), 2573–2586 (2016)

    MathSciNet  MATH  Google Scholar 

  15. 15.

    Colom, M.: Multiscale noise estimation and removal for digital images. Ph.D. thesis, Universitat de les Illes Balears (2014)

  16. 16.

    Coupé, P., Hellier, P., Kervrann, C., Barillot, C.: Nonlocal means-based speckle filtering for ultrasound images. IEEE TIP 18(10), 2221–2229 (2009)

    MathSciNet  MATH  Google Scholar 

  17. 17.

    Coupé, P., Yger, P., Barillot, C.: Fast non local means denoising for 3d mr images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 33–40. Springer (2006)

  18. 18.

    Coupé, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., Barillot, C.: An optimized blockwise nonlocal means denoising filter for 3-d magnetic resonance images. IEEE TMI 27(4), 425–441 (2008)

    Google Scholar 

  19. 19.

    Dabov, K., Foi, A., Egiazarian, K.: Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE TIP 16(8), 2080–2095 (2007)

    MathSciNet  Google Scholar 

  20. 20.

    Dabov, K., Foi, A., Egiazarian, K.: Video denoising by sparse 3d transform-domain collaborative filtering. In: 2007 15th European Signal Processing Conference, pp. 145–149. IEEE (2007)

  21. 21.

    Davy, A., Ehret, T., Facciolo, G., Morel, J., Arias, P.: Non-local video denoising by CNN. CoRR arXiv:1811.12758 (2018)

  22. 22.

    Davy, A., Ehret, T., Facciolo, G., Morel, J., Arias, P.: A non-local cnn for video denoising. In: IEEE ICIP, pp. 2409–2413 (2019)

  23. 23.

    De Fontes, F.P.X., Barroso, G.A., Coupé, P., Hellier, P.: Real time ultrasound image denoising. J. Real-Time Image Process. 6(1), 15–22 (2011)

    Article  Google Scholar 

  24. 24.

    Duval, V., Aujol, J.F., Gousseau, Y.: On the parameter choice for the non-local means (2010)

  25. 25.

    Ehmann, J., Chu, L.C., Tsai, S.F., Liang, C.K.: Real-time video denoising on mobile phones. In: IEEE ICIP, pp. 505–509 (2018)

  26. 26.

    Ehret, T., Arias, P.: Implementation of the vbm3d video denoising method and some variants. CoRR arXiv:2001.01802 (2020)

  27. 27.

    Ehret, T., Arias, P., Morel, J.M.: Global patch search boosts video denoising. VISAPP 5, 124–134 (2017)

    Google Scholar 

  28. 28.

    Ehret, T., Davy, A., Morel, J.M., Facciolo, G., Arias, P.: Model-blind video denoising via frame-to-frame training. In: IEEE CVPR, pp. 11369–11378 (2019)

  29. 29.

    Ehret, T., Morel, J.M., Arias, P.: Non-local kalman: A recursive video denoising algorithm. In: IEEE ICIP, pp. 3204–3208 (2018)

  30. 30.

    Franzen, R.: Kodak lossless true color image suite. http://r0k.us/graphics/kodak4 (1999)

  31. 31.

    Frosio, I., Kautz, J.: Statistical nearest neighbors for image denoising. IEEE TIP 28(2), 723–738 (2018)

    MathSciNet  MATH  Google Scholar 

  32. 32.

    Gilboa, G., Osher, S.: Nonlocal linear image regularization and supervised segmentation. Multiscale Model Simul. 6(2), 595–630 (2007)

    MathSciNet  Article  Google Scholar 

  33. 33.

    Goossens, B., Luong, H., Aelterman, J., Pižurica, A., Philips, W.: A gpu-accelerated real-time NLmeans algorithm for denoising color video sequences. In: ACIVS, pp. 46–57. Springer (2010)

  34. 34.

    Gu, S., Zhang, L., Zuo, W., Feng, X.: Weighted nuclear norm minimization with application to image denoising. In: IEEE CVPR, pp. 2862–2869 (2014)

  35. 35.

    Honzátko, D., Kruliš, M.: Accelerating block-matching and 3d filtering method for image denoising on GPUs. J. Real-Time Image Process. 16(6), 2273–2287 (2019)

    Article  Google Scholar 

  36. 36.

    Honzátko, D., Kruliš, M.: Cuda implementation of bm3d. https://github.com/DawyD/bm3d-gpu (2018)

  37. 37.

    Jin, Q., Grama, I., Kervrann, C., Liu, Q.: Nonlocal means and optimal weights for noise removal. SIAM SIIMS 10(4), 1878–1920 (2017)

    MathSciNet  Article  Google Scholar 

  38. 38.

    Junkins, S.: The compute architecture of intel® processor graphics gen9 (2015)

  39. 39.

    Kervrann, C., Boulanger, J., Coupé, P.: Bayesian non-local means filter, image redundancy and adaptive dictionaries for noise removal. In: International Conference on Scale Space and Variational Methods in Computer Vision, pp. 520–532. Springer (2007)

  40. 40.

    Lebrun, M.: An analysis and implementation of the BM3D image denoising method. IPOL 2, 175–213 (2012)

    Article  Google Scholar 

  41. 41.

    Lebrun, M., Buades, A., Morel, J.M.: A nonlocal bayesian image denoising algorithm. SIAM SIIMS 6(3), 1665–1688 (2013)

    MathSciNet  Article  Google Scholar 

  42. 42.

    Lehtinen, J., Munkberg, J., Hasselgren, J., Laine, S., Karras, T., Aittala, M., Aila, T.: Noise2noise: Learning image restoration without clean data. In: International Conference on Machine Learning, pp. 2971–2980 (2018)

  43. 43.

    Ma, K., Duanmu, Z., Wu, Q., Wang, Z., Yong, H., Li, H., Zhang, L.: Waterloo exploration database: new challenges for image quality assessment models. IEEE TIP 26(2), 1004–1016 (2017)

    MathSciNet  MATH  Google Scholar 

  44. 44.

    Maggioni, M., Boracchi, G., Foi, A., Egiazarian, K.: Video denoising, deblocking, and enhancement through separable 4-D nonlocal spatiotemporal transforms. IEEE TIP 21(9), 3952–3966 (2012)

    MathSciNet  MATH  Google Scholar 

  45. 45.

    Mahmoudi, M., Sapiro, G.: Fast image and video denoising via nonlocal means of similar neighborhoods. IEEE SPL 12(12), 839–842 (2005)

    Google Scholar 

  46. 46.

    Makitalo, M., Foi, A.: Optimal inversion of the generalized anscombe transformation for Poisson–Gaussian noise. IEEE TIP 22(1), 91–103 (2012)

    MathSciNet  MATH  Google Scholar 

  47. 47.

    Márques, A., Pardo, A.: Implementation of non local means filter in GPUs. In: Iberoamerican Congress on Pattern Recognition, pp. 407–414. Springer (2013)

  48. 48.

    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of 8th International Conference on Computer Vision, vol. 2, pp. 416–423 (2001)

  49. 49.

    NVIDIA: NVIDIA OpenCL Best Practices Guide (2009)

  50. 50.

    Pfleger, S.G., Plentz, P.D.M., Rocha, R.C.O., Pereira, A.D., Castro, M.: Real-time video denoising on multicores and gpus with kalman-based and bilateral filters fusion. J. of Real-Time Image Process. 16(5), 1629–1642 (2017)

    Article  Google Scholar 

  51. 51.

    Sutour, C., Deledalle, C.A., Aujol, J.F.: Adaptive regularization of the NL-means: application to image and video denoising. IEEE TIP 23(8), 3506–3521 (2014)

    MathSciNet  MATH  Google Scholar 

  52. 52.

    Wang, J., Guo, Y., Ying, Y., Liu, Y., Peng, Q.: Fast non-local algorithm for image denoising. In: IEEE ICIP, pp. 1429–1432 (2006)

  53. 53.

    Wang, T., Sun, Y.: GPU-accelerated denoising with bm3d. https://github.com/JeffOwOSun/gpu-bm3d (2017)

  54. 54.

    Wang, X., Xu, K., Wang, D.: Accelerating block-matching and 3d filtering-based image denoising algorithm on fpgas. In: IEEE ICSP, pp. 235–240. IEEE (2018)

  55. 55.

    Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: residual learning of deep cnn for image denoising. IEEE TIP 26(7), 3142–3155 (2017)

    MathSciNet  MATH  Google Scholar 

  56. 56.

    Zhang, K., Zuo, W., Zhang, L.: Ffdnet: toward a fast and flexible solution for cnn-based image denoising. IEEE TIP 27(9), 4608–4622 (2018)

    MathSciNet  Google Scholar 

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Acknowledgements

The authors gratefully thank Jean-Michel Morel for his valuable feedbacks. Work partly financed by IDEX Paris-Saclay IDI 2016, ANR-11-IDEX-0003-02, Office of Naval research grant N00014-17-1-2552, DGA Astrid project «filmer la Terre» no ANR-17-ASTR-0013-01, MENRT and Fondation Mathématique Jacques Hadamard.

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Correspondence to Axel Davy.

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Davy, A., Ehret, T. GPU acceleration of NL-means, BM3D and VBM3D. J Real-Time Image Proc 18, 57–74 (2021). https://doi.org/10.1007/s11554-020-00945-4

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Keywords

  • Image denoising
  • Video denoising
  • OpenCL
  • GPU
  • NL-means
  • BM3D
  • VBM3D