Free-size accelerated Kuwahara filter


Kuwahara filter is a smoothing filter used in image processing for adaptive noise reduction that has the ability to preserve object edges. Applications for this filter exist in fields such as medical imaging and artistic imaging. However, it has a very high computational cost, especially when filter size is large. In this paper, we propose an efficient algorithm to accelerate this filter regardless of filter size. Our method uses Summed-area Table (SAT) to gain fast computation of mean and variance values in Kuwahara filter. After that, three acceleration methods, namely caching mean and variance values, memory access optimization and flexible-format SATs are proposed to optimize the SAT-based Kuwahara filter. The experiments show that our method achieves the lowest possible big-O time complexity and performs at around the same level regardless of filter size, while producing the exact same output image as the original method. The achieved speedup ratio grows quadratically with the filter size, ranging from 10x to 1000x and even more. As a result, our optimized filter can run at a high frame rate even when the filter is large.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13


  1. 1.

    Wen, J., Zhang, Z., Zhang, Z., Fei, L., and Wang, M.: Generalized incomplete multiview clustering with flexible locality structure diffusion. IEEE Transactions on Cybernetics, pages 1–14, (2020)

  2. 2.

    Clark, A., and Piumsomboon. T.: A realistic augmented reality racing game using a depth-sensing camera. Proceedings of the 10th International Conference on Virtual Reality Continuum and Its Applications in Industry - VRCAI ’11, (2011)

  3. 3.

    Lei, Y., Yuan, W., Wang, H., Wenhu, Y., Bo, W.: A skin segmentation algorithm based on stacked autoencoders. IEEE Transactions on Multimedia 19(4), 740–749 (2017)

    Article  Google Scholar 

  4. 4.

    Joshua, O., Ibiyemi, T., and Adu, B.: A comprehensive review on various types of noise in image processing. International Journal of Scientific and Engineering Research, 10:388–393, 11 (2019)

  5. 5.

    Söderman, M., Holmin, S., Andersson, T., Palmgren, C., Babić, D., Hoornaert, B.: Image noise reduction algorithm for digital subtraction angiography: clinical results. Radiology 269(2), 553–560 (2013)

    Article  Google Scholar 

  6. 6.

    Leipsic, J., LaBounty, T.M., Heilbron, B., Min, J.K., Mancini, G.B.J., Lin, F.Y., Taylor, C., Dunning, A., Earls, J.P.: Adaptive statistical iterative reconstruction: assessment of image noise and image quality in coronary CT angiography. American Journal of Roentgenology 195(3), 649–654 (2010)

    Article  Google Scholar 

  7. 7.

    Papari, G., Petkov, N., and Campisi, P.: Edge and corner preserving smoothing for artistic imaging. Proceedings of the International Society for Optical Engineering, (2007)

  8. 8.

    Fan, L., Zhang, F., Fan, H., and Zhang, C.: Brief review of image denoising techniques. Visual Computing for Industry, Biomedicine, and Art, 2(1), (2019)

  9. 9.

    Irum, I., Shahid, M., Sharif, M., and Raza, M.: A review of image denoising methods. Journal of Engineering Science and Technology Review, 8:41–48, 12 (2015)

  10. 10.

    EPIPHANY, J. L., and Danasingh, A. A.: Salt and pepper noise detection and removal in gray scale images: An experimental analysis. International Journal of Signal Processing, Image Processing and Pattern Recognition, 6:343–352, 10 (2013)

  11. 11.

    Das, S., Saikia, J., Das, S., Goni, N.: A comparative study of different noise filtering techniques in digital images. International Journal of Engineering Research and General Science 3(5), 180–191 (2015)

    Google Scholar 

  12. 12.

    Chen, L., Jiang, F., Zhang, H., Wu, S., Yu, S., and Xie, Y.: Edge preservation ratio for image sharpness assessment. In 2016 12th World Congress on Intelligent Control and Automation (WCICA), pages 1377–1381. IEEE, (2016)

  13. 13.

    Kuwahara, M., Hachimura, K., Eiho, S., and Kinoshita, M.: Processing of ri-angiocardiographic images. Digital Processing of Biomedical Images, pages 187–202, (1976)

  14. 14.

    Kyprianidis, J.E., Kang, H., Döllner, J.: Image and video abstraction by anisotropic Kuwahara filtering. Computer Graphics Forum 28(7), 1955–1963 (2009)

    Article  Google Scholar 

  15. 15.

    Bartyzel, K.: Adaptive Kuwahara filter. Signal, Image and Video Processing 10(4), 663–670 (2015)

    Article  Google Scholar 

  16. 16.

    Gao, J., Li, D., and Gao, W.: Oil painting style rendering based on kuwahara filter. IEEE Access, 7, (2019)

  17. 17.

    Zhang, K., Zuo, W., and Zhang, L.: Learning a single convolutional super-resolution network for multiple degradations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2018)

  18. 18.

    Zhang, K., Zuo, W., Zhang, L.: Ffdnet: Toward a fast and flexible solution for cnn-based image denoising. IEEE Transactions on Image Processing 27(9), (2018)

  19. 19.

    Crow, F. C.: Summed-area tables for texture mapping. Proceedings of the 11th annual conference on Computer graphics and interactive techniques - SIGGRAPH ’84, (1984)

  20. 20.

    Elmasry, A., and Katajainen, J.: Branchless search programs. Experimental Algorithms, pages 127–138, (2013)

  21. 21.

    Intel\(\textregistered\) 64 and ia-32 architectures optimization reference manual., 2020

  22. 22.

    Fog, A.: Lists of instruction latencies, throughputs and micro-operation breakdowns for intel, amd, and via cpus., 2019

  23. 23.

    Carvalho, C.: The gap between processor and memory speeds. 01 (2002)

  24. 24.

    Hillis, W.D., Steele, G.L.: Data parallel algorithms. Communications of the ACM 29(12), 1170–1183 (1986)

    Article  Google Scholar 

  25. 25.

    Sengupta, S., Lefohn, A., and Owens, J.: A work-efficient step-efficient prefix sum algorithm. 05 (2006)

  26. 26.

    Jégou, H., Douze, M., Schmid, C., COG, T., and Lear, E.-P.: Hamming embedding and weak geometry consistency for large scale image search - extended version. 10 (2008)

  27. 27.

    Soares, L., Tam, D., and Stumm, M.: Reducing the harmful effects of last-level cache polluters with an os-level, software-only pollute buffer. In 2008 41st IEEE/ACM International Symposium on Microarchitecture, pages 258–269, (2008)

  28. 28.

    Zhu, Y., Huang, C.: An improved median filtering algorithm for image noise reduction. Physics Procedia 25, 609–616 (2012)

    Article  Google Scholar 

Download references


The authors would like to thank Vietnam Academy of Science and Technology (VAST) for funding this research in the scope of project DL0000.05/20-22.

Author information



Corresponding author

Correspondence to Giang Son Tran.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Le, H.D., Tran, G.S. Free-size accelerated Kuwahara filter. J Real-Time Image Proc (2021).

Download citation


  • Kuwahara filter
  • Smoothing filter
  • Summed-area table
  • Time complexity