Abstract
Halftoning is a necessary technique for electrophotographic printers to print continuous tone images. Scanned images obtained from such printed hard copies are corrupted by screen like artifacts called halftone patterns. Descreening aims to recover high quality continuous tone image from the scanned image. In this paper, a two-step descreening method is proposed to remove screen like artifacts adaptively. Firstly, an Extreme Learning Machine (ELM) based halftone image classification scheme is introduced to categorize the scanned images into different resolutions. Then in the halftone pattern removal step, patch similarity based smoothing filtering and nonlinear enhancement are combined to remove halftone patterns and preserve the image quality. Experiments demonstrate that the proposed method removes halftone patterns effectively, while preserving more details and recovering cleaner smoothing regions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Sullivan, J., Ray, L., Miller, R.L.: Design of Minimum Visual Modulation Halftone Patterns. IEEE Trans. Syst., Man, Cybern. 21(1), 33–38 (1991)
Siddiqui, H., Bouman, C.A.: Training-Based Descreening. IEEE Trans. Image Process. 16(3), 789–802 (2007)
Stevenson, R.L.: Inverse halftoning via MAP estimation. IEEE Trans. Image Process. 4(4), 486–498 (1997)
Chang, P., Yu, C., Lee, T.: Hybrid LMS-MMSE Inverse Halftoning Technique. IEEE Trans. Image Process. 10(1), 95–103 (2001)
Chen, L., Hang, H.: An Adaptive Inverse Halftoning Algorithm. IEEE Trans. Image Process. 6(8), 1202–1209 (1997)
Liu, Y., Guo, J., Lee, J.: Inverse Halftoning Based on the Bayesian Theorem. IEEE Trans. Image Process. 20(4), 1077–1084 (2011)
Siddiqui, H., Bouman, C.A.: Hardware-Friendly Descreening. IEEE Trans. Image Process. 19(3), 746–757 (2010)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006)
Chen, X., Kang, S.B., Yang, J., Yu, J.: Fast Patch-Based Denoising Using Approximated Patch Geodesic Paths. In: Proc. IEEE Conf. Comput. Vision Pattern Recog., pp.1211–1218 (2013)
He, K., Sun, J., Tang, X.: Guided Image Filtering. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 1–14. Springer, Heidelberg (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, F., Li, S., Xu, L., Sun, B., Sun, J. (2014). A Two-Step Adaptive Descreening Method for Scanned Halftone Image. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-662-45643-9_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45642-2
Online ISBN: 978-3-662-45643-9
eBook Packages: Computer ScienceComputer Science (R0)