Journal of Real-Time Image Processing

, Volume 16, Issue 1, pp 31–47 | Cite as

Kernel design for real-time denoising implementation in low-resolution images

  • Sun Young Jung
  • Yun Joo Chyung
  • Pyoung Won KimEmail author
Special Issue Paper


Upsampling and removing noise from digital images are important tasks in image processing. Single-image upsampling with denoising influences the quality of the resulting images. Image upsampling is known as superresolution, which refers to restoration of a higher-resolution image from a given low-resolution image. In this paper, we propose a filter-based image upsampling and denoising method for low-resolution images. The proposed method involves two stages. In the first stage, we design least squares method-based filters. In the second stage, we implement an image upsampling and denoising process. The proposed method is compared with several standard benchmark methods, including the nearest neighbor, bilinear, and bicubic methods, to test whether it yields better restoration quality and computational advantages. In addition, we design various-sized filters and test them on low-resolution noisy images. From the experimental results, we conclude that filters with more taps return better results, but longer computational running times. The quality of the image upsampling and denoising of the tested methods is compared subjectively and objectively through simulation. The simulation results suggest how the user can best select an appropriate filter size to achieve optimal trade-off results.


Denoising Multimedia Immersion Noise Artificial intelligence Image display 



This work was supported by the Institutes of Convergence Science and Technology, Incheon National University Research Grant in 2016.

Compliance with ethical standards

Conflict of interest

Authors Sun Young Jung, Yun Joo Chyung, and Pyoung Won Kim declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. 1.
    Hardie, R.C., Barnard, K.J., Armstrong, E.A.: Joint map registration and high-resolution image estimation using a sequence of undersampled images. IEEE Trans. Image Process. 6(12), 1621–1633 (1997)CrossRefGoogle Scholar
  2. 2.
    Tipping, M.E., Bishop, C.M.: Bayesian image super-resolution. In: Proceedings of Advances in Neural Information Processing Systems, vol. 16, pp. 1303–1310 (2003)Google Scholar
  3. 3.
    Dai, S., Han, M., Xu, W., Wu, Y., Gong, Y.: Soft edge smoothness prior for alpha channel super resolution. In: Proceedings of IEEE Conference on Computer Vision and Pattern Class, pp. 1–8 (2007)Google Scholar
  4. 4.
    Sun, J., Xu, Z., Shum, H.: Image super-resolution using gradient profile prior. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)Google Scholar
  5. 5.
    Chang, H., Yeung, D.-Y., Xiong, Y.: Super-resolution through neighbor embedding. In: Proceedings of IEEE Conference on Computer Vision and Pattern Class, vol. 1, pp. 275–282 (2004)Google Scholar
  6. 6.
    Baker, S., Kanade, T.: Hallucinating faces. In: Proceedings of IEEE International Conference on Automatic Face Recognition, pp. 83–88 (2000)Google Scholar
  7. 7.
    Candes, E.J.: Compressive sampling. In: Proceedings of the International Congress of Mathematicians, vol. 3, pp. 1433–1452 (2006)Google Scholar
  8. 8.
    Wu, J., Anisetti, M., Wu, W., Damiani, E., Jeon, G.: Bayer demosaicking with polynomial interpolation. IEEE Trans. Image Process. 25(11), 5369–5382 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Jeon, G., Anisetti, M., Kim, D., Bellandi, V., Damiani, E., Jeong, J.: Fuzzy rough sets hybrid scheme for motion and scene complexity adaptive deinterlacing. Image Vis. Comput. 27(4), 425–436 (2009)CrossRefGoogle Scholar
  10. 10.
    Paul, A.: Real-time power management for embedded M2M using intelligent learning methods. ACM Trans. Embed. Comput. Syst. 13(5s), 148 (2014)CrossRefGoogle Scholar
  11. 11.
    Paul, A., Rho, S., Bharanitharan, K.: Interactive scheduling for mobile multimedia service in M2M environment. Multimed. Tools Appl. 71(1), 235–246 (2014)CrossRefGoogle Scholar
  12. 12.
    Jeon, G., Anisetti, M., Bellandi, V., Damiani, E., Jeong, J.: Designing of a type-2 fuzzy logic filter for improving edge-preserving restoration of interlaced-to-progressive conversion. Inf. Sci. 179(13), 2194–2207 (2009)CrossRefGoogle Scholar
  13. 13.
    Baker, S., Kanade, T.: Limits on super-resolution and how to break them. IEEE Trans. Pattern Anal. Mach. Intell. 24(9), 1167–1183 (2002)CrossRefGoogle Scholar
  14. 14.
    Jeon, G., Anisetti, M., Lee, J., Bellandi, V., Damiani, E., Jeong, J.: Concept of linguistic variable-based fuzzy ensemble approach: application to interlaced HDTV sequences. IEEE Trans. Fuzzy Syst. 17(6), 1245–1258 (2009)CrossRefGoogle Scholar
  15. 15.
    Jeon, G., Park, S.J., Fang, Y., Anisetti, M., Bellandi, V., Damiani, E., Jeong, J.: Specification of efficient block matching scheme for motion estimation in video compression. SPIE Opt. Eng. 48(12), 127005 (2009)CrossRefGoogle Scholar
  16. 16.
    Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: Learning lowlevel vision. Int. J. Comput. Vis. 40(1), 25–47 (2000)CrossRefzbMATHGoogle Scholar
  17. 17.
    Wu, J., Huang, J., Jeon, G., Cho, J., Jeong, J., Jiao, L.: An adaptive autoregressive deinterlacing method. SPIE Opt. Eng. 50(5), 057001 (2011)CrossRefGoogle Scholar
  18. 18.
    Jeon, G., Anisetti, M., Bellandi, V., Damiani, E., Jeong, J.: Fuzzy weighted approach to improve visual quality of edge-based filtering. IEEE Trans. Consum. Electron. 53(4), 1661–1667 (2007)CrossRefGoogle Scholar
  19. 19.
    Wu, W., Yang, X., Pang, Y., Peng, J., Jeon, G.: A multifocus image fusion method by using hidden Markov model. Opt. Commun. 287, 63–72 (2013)CrossRefGoogle Scholar
  20. 20.
    Jeon, G., Anisetti, M., Bellandi, V., Damiani, E., Jeong, J.: Rough sets-assisted subfield optimization for alternating current plasma display panel. IEEE Trans. Consum. Electron. 53(3), 825–832 (2007)CrossRefGoogle Scholar
  21. 21.
    Jeon, G., Anisetti, M., Kang, S.: A rank-ordered marginal filter for deinterlacing. Sensors 13(3), 3056–3065 (2013)CrossRefGoogle Scholar
  22. 22.
    Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)CrossRefGoogle Scholar
  23. 23.
    Liu, C., Shum, H.Y., Freeman, W.T.: Face halluciantion: theory and practice. Int. J. Comput. Vis. 75(1), 115–134 (2007)CrossRefGoogle Scholar
  24. 24.
    Cho, C., Jeon, J., Paik, J.: Real-time spatially adaptive image restoration using truncated constrained least squares filter. In: Proceedings of IEEE ICCE 2014, pp. 256–257 (2014)Google Scholar
  25. 25.
    Angelopoulos, G., Pitas, I.: Multichannel Wiener filters in color image restoration. IEEE Trans. Circuits Syst. Video Technol. 4, 83–87 (1994)CrossRefGoogle Scholar
  26. 26.
    Angelopoulos, G., Pitas, I.: Multichannel image modelling in Wiener filter design for color image restoration. In: Proceedings. 6th Mediterranean Electrotechnical Conference 1991, vol. 2, pp. 1224–1227 (1991)Google Scholar
  27. 27.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  28. 28.
    Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: BM3D image denoising with shape-adaptive principal component analysis. In: Proceedings of Workshop on Signal Processing with Adaptive Sparse Structured Representations (SPARS’09), Saint-Malo, France, April 2009Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Institutes of Convergence Science and TechnologyIncheon National UniversityIncheonRepublic of Korea

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