Abstract
Specular detection and removal has been a hot topic in the field of computer vision. Most of the existing methods are mainly for color images, but grayscale images are widely used. For a single grayscale image with only intensity information, highlight detection and removal becomes a difficult issue. To solve this problem, the single grayscale image highlight detection and removal method based on Markov random field is presented. Each reflection component modeling is estimated by geometric relation of surface normal in diffuse and specular reflection component in the framework of Markov random field. Their maximum a posteriori estimation is calculated under Bayesian formula and highlight area is detected. Finally, image inpainting method based on the BSCB model removes highlights. Experiment reveals that this method can effectively detect grayscale image specular reflection area, improve highlight areas the repair rate.
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
References
Tan, P., Lin, S., Quan, L., Shum, H.Y.: Highlight removal by illumination-constrained inpainting. In: IEEE International Conference on Computer Vision, vol. 1, pp. 164—169. IEEE Press, Nice (2003)
Schluns, K., Koschan, A.: Global and local highlight analysis in color images. In: Proceedings of 1st International Conference on Color Graphics Image Processing, pp. 300–304 (2000)
Lee, H.-C.: Method for computing the scene-illuminant chromaticity from specular highlights. J. Opt. Soc. Am. A: 3, 1694–1699 (1986)
Tan, P., Quan, L., Lin, S.: Separation of highlight reflections on textured surfaces. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1855–1860. IEEE Press, New York (2006)
Yoon, K.J., Choi, Y., Kweon, I.S.: Fast separation of reflection components using a specularity-invariant image representation. In: 2006 IEEE International Conference on Image Processing, pp. 973–976. IEEE Press, Atlanta (2006)
Yang, Q., Wang, S., Ahuja, N.: Real-time specular highlight removal using bilateral filtering. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 87–100. Springer, Heidelberg (2010)
Klinker, G.J., Shafer, S.A., Kanade, T.: The measurement of highlights in color images. Int. J. Comput. Vis. 2, 7–32 (1990)
Shafer, S.A.: Using color to separate reflection components. Color Res. Appl. 10, 210–218 (1985)
Noak, C.L., Shafer, S.A.: Anatomy of a color histogram. In: Proceedings of the IEEE Computer Vision and Pattern Recognition, pp. 599–605. IEEE Press, Champaign (1992)
Acknowledgments
This work was financially supported by National Natural Science Foundation of China (61440025), the research project of science and technology of Heilongjiang provincial education department (12541119).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Yin, F., Chen, T., Wu, R., Fu, Z., Yu, X. (2016). Specular Detection and Removal for a Grayscale Image Based on the Markov Random Field. In: Che, W., et al. Social Computing. ICYCSEE 2016. Communications in Computer and Information Science, vol 623. Springer, Singapore. https://doi.org/10.1007/978-981-10-2053-7_57
Download citation
DOI: https://doi.org/10.1007/978-981-10-2053-7_57
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-2052-0
Online ISBN: 978-981-10-2053-7
eBook Packages: Computer ScienceComputer Science (R0)