Skip to main content
SpringerLink
Log in

CDDA: color-dominant deep autoencoder for faster and efficient bilateral image filtering

  • Original Article
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Nonlinear processing of high-dimensional data is quite common in image filtering algorithms. Bilateral, joint bilateral, and non-local means filters are the examples of the same. Real-time implementation of high-dimensional filters has always been a research challenge due to its computational complexity. In this paper, we have proposed a solution utilizing both color sparseness and color dominance in an image which ensures a faster algorithm for generic high-dimensional filtering. The solution speeds up the filtering algorithm further by psycho-visual saliency-based deep encoded dominant color gamut, learned for different subject classes of images. The proposed bilateral filter has been proved to be efficient both in terms of psycho-visual quality and performance for edge-preserving smoothing and denoising of color images. The results demonstrate competitiveness of our proposed solution with the existing fast bilateral algorithms in terms of the CTQ (critical to quality) parameters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: International Conference on Computer Vision, pp. 839–846 (1998)

  2. Das, A., Nair, P., Shylaja, S.S., Chaudhury, K.N.: A concise review of fast bilateral filtering. In: 2017 Fourth International Conference on Image Information Processing (ICIIP), 1–6 (2017)

  3. Das, A.: Guide to Signals and Patterns in Image Processing. Springer, Berlin (2015)

    Book  Google Scholar 

  4. Gastal, E.S.L., Oliveira, M.M.: Adaptive manifolds for real-time high-dimensional filtering. SIGGRAPH (ACM TOG) 31(4), 1–13 (2012)

    Article  Google Scholar 

  5. Nair, P., Chaudhury, K.N.: Fast high-dimensional filtering using clustering. In: International Conference of Image Processing (2017)

  6. Durand, F., Dorsey, J.: Fast bilateral filtering for the display of high-dynamic-range images. ACM Trans. Graph. 21, 257–266 (2002)

    Article  Google Scholar 

  7. Yang, Q., Tan, K. H., Ahuja, N.: Real-time O(1) bilateral filtering. In: CVPR 2009. IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 557–564. IEEE (2009)

  8. Yang, Q., Tan, K.H., Ahuja, N.: Constant time median and bilateral filtering. Int. J. Comput. Vision 112, 307–318 (2015)

    Article  Google Scholar 

  9. Mozerov, M.G., van de Weijer, J.: Global color sparseness and a local statistics prior for fast bilateral filtering. IEEE Trans. Image Process. 24, 5842–5853 (2015)

    Article  MathSciNet  Google Scholar 

  10. Shen, X., Chen, Y.C., Tao, X., Jia, J.: Convolutional neural pyramid for image processing. ArXiv e-prints (2017)

  11. Gharbi, M., Chen, J., Barron, J.T., Hasinoff, S.W., Durand, F.: Deep bilateral learning for real-time image enhancement. ACM Trans. Gr (TOG) 36(4), 118 (2017)

    Google Scholar 

  12. Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The feret evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090–1104 (2000)

    Article  Google Scholar 

  13. Guo, X., Liu, X., Zhu, E., Yin, J.: Deep clustering with convolutional autoencoders. In: International Conference on Neural Information Processing (2017)

  14. Ye, M., Giannarou, S., Meining, A., Yang, G.Z.: Online tracking and retargeting with applications to optical biopsy in gastrointestinal endoscopic examinations. Med. Image Anal. (2015)

  15. Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. Presented at the (2015)

  16. Das, A., Ajithkumar, N.: Engineering the perception of recognition through interactive raw primal sketch by HNFGS and CNN-MRF. In: Computer Vision and Image Processing (CVIP). Springer, Berlin (2017)

  17. Konar, A.: Computational Intelligence: Principles, Techniques and Applications. Springer, Berlin (2005)

    Book  Google Scholar 

  18. Das, A.: Bacterial foraging optimization for digital filter synthesis: a computational intelligence approach to dsp and image processing. Lambert Academic Publishing, Cambridge (2013)

    Google Scholar 

Download references

Acknowledgements

The authors are thankful to Pallavi Saha for helping us in data collection, annotation, and simulation.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, PES University, Bangalore, India

    Apurba Das & S. S. Shylaja

Authors
  1. Apurba Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. S. Shylaja
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Apurba Das.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Das, A., Shylaja, S.S. CDDA: color-dominant deep autoencoder for faster and efficient bilateral image filtering. SIViP 15, 1189–1195 (2021). https://doi.org/10.1007/s11760-020-01848-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-020-01848-4

Keywords

Search

Navigation