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A New Smoothing Based Image Recolorization Method

Published: 24 August 2019 Publication History

Abstract

Image recolorization is a process of generating new synthetic images for given reference images and input images. Color characteristics and geometrical structure details of the synthetic images are transferred from reference images and input images, respectively. In classical image recolorization models, the total variation (TV) regularizer is usually used to suppress noise and graininess during the process of estimating synthetic images. However, the TV regularizer usually produces pseudo contours known as staircase-like artifacts and cannot preserve some important structure details well. To solve these problems, in this paper a new smoothing based image recolorization model is proposed, in which a fractional-order TV regularizer is designed. Moreover, an edge protection process is also proposed which can further improve the preservation performance of image tiny details. Numerical results demonstrate that our proposed model can effectively protect image tiny details of image recolorization results, while the staircase-like artifacts are avoided.

References

[1]
Huang, J. B., Chen, C. S., Jen, T. C., and Wang, S. J. Image recolorization for the colorblind. In IEEE International Conference on Acoustics, Speech and Signal Processing. Taipei, Taiwan, 2009, 1161--1164. DOI=https://10.1109/ ICASSP.2009.4959795.
[2]
Reinhard, E., Ashikhmin, M., Gooch, B., and Shirle, P. Color Transfer between Images. IEEE Comput. Graph. Vol. 21, No. 5, 2001, 34--41. DOI= https://doi.org/ 10.1109/38.946629.
[3]
Sýkora, D., Burianek, J., and Zara. J. Colorization of black-and-white cartoons. Image. Vision Comput.vol.23,No.9,2005, 767--782. DOI=https://doi.org/10.1109/tmm.2012. 2188997.
[4]
Irony, R., Cohen-Or, D. D., and Lischinski, D. Colorization by example. In Proceedings of Eurographics Symposium on Rendering Techniques. Konstanz, GERMANY, 2005, 201--210. DOI=https://doi.org/ 10.2312/EGWR/EGSR05/201--210.
[5]
Wen,C. L., Chang, H., Chen,B. Y., and Ouhyoung, M. Example-based multiple local color transfer by Strokes. Comput. Graph. Forum. Vol. 27, No.7, 2008, 1765--1772. DOI=https://doi. org/ 10.1111/j.1467--8659.2008.01321.x.
[6]
Zhang, Q., Xiao, C., Sun, H. Q., and Tang, F. Palette-based image recoloring using color decomposition optimization. IEEE Trans. Image Proc. Vol.26, No.4, 2017, 1952--1964. DOI=https://doi.org/ 10.1109/TIP.2017.2671779.
[7]
Neumann, A. Color style transfer techniques using hue, lightness and saturation histogram matching. In Proceedings of Computational Aestetics in Graphics, Visualization and Imaging, Girona. SPAIN, 2005, 111--122. DOI=https://doi. org/ 10.2312/COMPAESTH/COMPAESTH05/111--122.
[8]
Pitie, F., Kokaram, A. C., Dahyot, R. N-dimensional probability density function transfer and its application to colour Transfer. In Proceedings of IEEE International Conference on Computer Vision, Beijing, CHINA, 2005, 1434--439. DOI=https://doi.org/ 10.1109/ICCV.2005.166.
[9]
Tian, D., Xue, D. Y., and Wang, D. H. A fractional-order adaptive regularization primal dual algorithm for image denoising. Inform. Sciences. Vol. 296, No.5, 2015, 147--159. DOI=https://doi.org/10.1016/j.ins.2014.10.050.
[10]
Chen,G., Zhang, J., and Li, D. Robust Kronecker product video denoising based on fractional-order total variation model. Signal Process. Vol. 119, No.2, 2016, 1--20. DOI=https://doi. org/10.1016/j.sigpro.2015.06.027.
[11]
Li, C., Xu, C., Gui, C., and Fox, M. D. Distance regularized level set evolution and its application to image segmentation. IEEE Trans. Image Process. Vol. 19, No.12, 2010, 3243--3254. DOI=https://doi.org/10.1109/TIP.2010.2069690.
[12]
Han, Y., Xu, C., Baciu, G., Li, M., and Islam, M. Cartoon and texture decomposition based color transfer for fabric images. IEEE Trans. Multimedia. Vol. 69, No.9, 2015, 1291--1296. DOI=https://doi.org/10.1109/TMM.2016.2608000.
[13]
Kang, S. H., and March, R. Variational models for image colorization via chromaticity and brightness decomposition. IEEE Trans. Image Proc. Vol.16, No.9, 2007, 2251--2261. DOI=https://doi.org/10.1109/TIP.2007.903257.
[14]
Papadakis, N., Provenzi, E., and Caselles, V. A variational model for histogram transfer of color images. IEEE Trans. Image Proc. Vol. 20, No.6, 2011, 1682--1695. DOI=https:// doi.org/10. 1109/tip.2010.2095869.
[15]
Rabin, J., and Peyré, G. Wasserstein regularization of imaging problem. In Proceedings of IEEE International Conference on Image Process. Brussels, BE, 2011, 1541--1544. DOI=https://doi.org/10.1109/ICIP.2011.6115740.
[16]
Xie, B., Xu, C., Han, Y., and Teng, R. F. Color Transfer Using Adaptive Second-order Total Generalized Variation Regularizer. IEEE Access. Vol. 6, No.3, 2017, 6829--6839. DOI=https://doi.org/10.1109/ACCESS.2018.2789981.
[17]
Afonso, M., Bioucas-Dias, J., and Figueiredo, M. An augmented Lagrangian approach to the constrained optimization formulation of imaging inverse problems. IEEE Trans. Image Process. Vol.20, No.3, 2011, 681--695. DOI=https://doi.org/10.1109/TIP.2010.2076294.
[18]
Chambolle, A., and Pock, T. A first-order primal-dual algorithm for convex problems with applications to imaging. J. Math. Imaging Vis. Vol. 40, No.1, 2011, 120--145. DOI=https://doi. org/10.1007/s10851-010-0251-1.
[19]
Han, X. T., Zhang, C. Y., Lin, W. Y., Xu, M. L., Sheng, B., and Mei, T. Tree-based visualization and optimization for image collection. IEEE Trans. Cybernetics. Vol.46, No.6, 2016, 1286--1300. DOI=https://doi.org/10.1109/TCYB.2015. 2448236.

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    ISICDM 2019: Proceedings of the Third International Symposium on Image Computing and Digital Medicine
    August 2019
    370 pages
    ISBN:9781450372626
    DOI:10.1145/3364836
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 24 August 2019

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    Author Tags

    1. Image recolorization
    2. augmented Lagrangian method
    3. fractional-order total variation

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    • the National Natural Science Foundation of China

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