Abstract
For Chinese rubbing image under the complex background, to against its characteristic of low contrast and large noise, we develop a gamma correction enhancement algorithm in which the grayscale space is being conducted before the Otsu’s binarization, experiments on multiple pictures show the superiority of the algorithm. At first, the global contrast is enhanced based on the gamma correct algorithm for the complement image of the Chinese rubbing image. After that, we have implemented optimum global thresholding using Otsu’s method for image segmentation. The experimental results show that our algorithm could correct the background noise of the image and enhance the stroke in the low contrast Chinese rubbing image, and there is no need to denoise in advance. The performance of the algorithm is simple, fast, and produces very good segmentation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Huang, Z.K., Li, Z.H., Huang, H., et al.: Comparison of different image denoising algorithms for Chinese calligraphy images. Neurocomputing 188, 102–112 (2016)
Panetta, K.A., Wharton, E.J., Agaian, S.S.: Human visual system-based image enhancement and logarithmic contrast measure. IEEE Trans. Syst. Man Cybern. B 38(1), 174–188 (2008)
Welfer, D., Scharcanski, J., Kitamura, C.M., et al.: Segmentation of the optic disk in color eye fundus images using an adaptive morphological approach. Comput. Biol. Med. 40(2), 124–137 (2010)
Guo, X., Li, Y., Ling, H.: LIME: low-light image enhancement via illumination map estimation. IEEE Trans. Image Process. 26(2), 982–993 (2017). https://doi.org/10.1109/TIP.2016.2639450
Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016). https://doi.org/10.1109/tpami.2015.2439281
Singh, G., Mittal, A.: Various image enhancement techniques-a critical review. Int. J. Innov. Sci. Res. 10(2), 267–274 (2014)
Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M.: Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imaging 8, 263–269 (1989)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (2007)
http://archive.fieldmuseum.org/chineserubbings/introduction_1.asp
Acknowledgements
This work was supported by the grants from the Educational Commission of Jiangxi province of China, No. GJJ151134.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Huang, H., Ma, YL. (2020). Efficient Segmentation Using Gamma Correction with Complement Image of Chinese Rubbing Image. In: Huang, DS., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12463. Springer, Cham. https://doi.org/10.1007/978-3-030-60799-9_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-60799-9_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60798-2
Online ISBN: 978-3-030-60799-9
eBook Packages: Computer ScienceComputer Science (R0)