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Inverse Halftoning Algorithm Based on SLIC Superpixels and DBSCAN Clustering

  • Fan Zhang
  • Zhenzhen Li
  • Xingxing Qu
  • Xinhong Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10956)

Abstract

Halftone technology is widely used in the printing industry. This paper proposes an inverse halftoning algorithm based on SLIC (Simple Linear Iterative Clustering) superpixels and DBSCAN (density-based spatial clustering of applications with noise) clustering. Firstly, halftoning image is segmented by SLIC superpixels algorithm. Then the boundaries region of image is tracked by DBSCAN clustering algorithm and the boundaries of image is vectored. Secondly, the remaining part of halftoning image that boundaries have been extracted is smoothed by linear and nonlinear smoothing filters. Finally the vector boundaries and the smooth background is combined together to get the inverse halftoning image. Experimental results show that the proposed method can effectively remove halftone patterns while retains boundaries information.

Keywords

Inverse halftoning SLIC superpixels DBSCAN clustering 

Notes

Acknowledgement

This research was supported by the Natural Science Foundation of China (Grant No. 61771006, No. U1504621); Natural Science Foundation of Henan Province (Grant No. 162300410032), and International Science and Technology Cooperation Project of Henan Province (Grant No. 144300510033).

References

  1. 1.
    Son, C.H., Choo, H.: Local learned dictionaries optimized to edge orientation for inverse halftoning. IEEE Trans. Image Process. 23(6), 2542–2556 (2014)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Son, C.H., Lee, K.W., Choo, H.: Inverse color to black-and-white halftone conversion via dictionary learning and color mapping. Inf. Sci. 299, 1–19 (2015)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., SuSstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  4. 4.
    Mese, M., Vaidyanathan, P.P.: Recent advances in digital halftoning and inverse halftoning methods. IEEE Trans. Circuits Syst. I Fundam. Theory Appl. 49(6), 790–805 (2002)CrossRefGoogle Scholar
  5. 5.
    Son, C.H.: Inverse halftoning based on sparse representation. Opt. Lett. 37(12), 2352–2354 (2012)CrossRefGoogle Scholar
  6. 6.
    Akyilmaz, E., Leloglu, U.M.: Segmentation of sar images using similarity ratios for generating and clustering superpixels. Electron. Lett. 52(8), 654–656 (2016)CrossRefGoogle Scholar
  7. 7.
    Xiang, D., Ban, Y., Wang, W., Su, Y.: Adaptive superpixel generation for polarimetric sar images with local iterative clustering and sirv model. IEEE Trans. Geosci. Remote Sens. 55(6), 3115–3131 (2017)CrossRefGoogle Scholar
  8. 8.
    Birant, D., Kut, A.: St-dbscan: An algorithm for clustering spatial-temporal data. Data Knowl. Eng. 60(1), 208–221 (2007)CrossRefGoogle Scholar
  9. 9.
    Zhong, C., Miao, D., Wang, R.: A graph-theoretical clustering method based on two rounds of minimum spanning trees. Pattern Recogn. 43(3), 752–766 (2010)CrossRefGoogle Scholar
  10. 10.
    Ester, M., Kriegel, H.P., Xu, X.: A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In: International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)Google Scholar
  11. 11.
    Hou, J., Gao, H., Li, X.: DSets-DBSCAN: a parameter-free clustering algorithm. IEEE Trans. Image Process. 25(7), 3182–3193 (2016)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Fan Zhang
    • 1
  • Zhenzhen Li
    • 1
  • Xingxing Qu
    • 1
  • Xinhong Zhang
    • 1
  1. 1.School of Computer and Information EngineeringHenan UniversityKaifengChina

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