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Reseach Article

Log-transform Weighted Total Variation for Image Smoothing

by Qiegen Liu, Li Zhu, Jianhua Wu
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 124 - Number 2
Year of Publication: 2015
Authors: Qiegen Liu, Li Zhu, Jianhua Wu
10.5120/ijca2015905411

Qiegen Liu, Li Zhu, Jianhua Wu . Log-transform Weighted Total Variation for Image Smoothing. International Journal of Computer Applications. 124, 2 ( August 2015), 35-40. DOI=10.5120/ijca2015905411

@article{ 10.5120/ijca2015905411,
author = { Qiegen Liu, Li Zhu, Jianhua Wu },
title = { Log-transform Weighted Total Variation for Image Smoothing },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 124 },
number = { 2 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 35-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume124/number2/22080-2015905411/ },
doi = { 10.5120/ijca2015905411 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:13:22.089151+05:30
%A Qiegen Liu
%A Li Zhu
%A Jianhua Wu
%T Log-transform Weighted Total Variation for Image Smoothing
%J International Journal of Computer Applications
%@ 0975-8887
%V 124
%N 2
%P 35-40
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Since the input image in computer vision and graphics containing various texture/structure patterns provides rich visual information, how to properly decompose them is a challenging problem. Recent developments in high-contrast detail smoothing show that how they define edges and how this prior information guides smoothing are two key points. In this paper, we present a novel Log-transform weighted total variation (LWTV) method, which employs the signed gradient summation of Log-transform pixels at neighbor window as data-fidelity weight. Specifically, LWTV substantially improves the decomposition for the regions with faint pixel-boundary and alleviates the drawback of slightly blurry. Experimental results demonstrate that the proposed method has appearance performance on image with abundant uniform textural details.

References
  1. L. Xu, Q. Yan, Y. Xia, J. Jia, “Structure extraction from texture via relative total variation,” ACM Trans. Graph., vol. 31, no. 6, 2012.
  2. L. Bao, Y. Song, Q. Yang, H. Yuan, G. Wang, “Tree filtering: efficient structure-preserving smoothing with a minimum spanning tree,” IEEE Trans. Image Process., vol. 23, no. 2, pp. 555–569, 2014.
  3. C. Tomasi, and R. Manduchi, “Bilateral filtering for gray and color images,” In ICCV, pp. 839–846. 1998.
  4. Z. Su, X. Luo, Z. Deng, Y. Liang, and Z. Ji, “Edge-preserving texture suppression filter based on joint filtering schemes,” IEEE Trans. Multimedia, vol. 15, no. 3, pp. 535–548, Apr. 2013.
  5. L. Karacan, E. Erdem and A. Erdem, “Structure preserving image smoothing via region covariances,” ACM Trans. Graph., vol. 32, no. 6, Nov. 2013.
  6. L. Rudin, S. Osher, E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D., vol. 60, pp. 259–68, 1992.
  7. Z.Farbman, R. Fattal, D. Lischinski, and R. Szeliski, “Edge-preserving decompositions for multi-scale tone and detail manipulation,” ACM Trans. Graph., vol. 27, no. 3, 2008.
  8. L. Xu, C. Lu, Y. Xu, J. Jia, “Image smoothing via L0 gradient minimization,” ACM Trans Graph., vol. 30, no. 6, 2011.
  9. R. C. Gonzalez, R. E. Woods, Digital Image Processing. 3rd ed., Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 2006.
  10. R. Fattal, D. Lischinski, and M. Werman, “Gradient domain high dynamic range compression,” In Proc. ACM SIGGRAPH, ACM, New York, NY, USA, pp. 249–256. 2002.
  11. P. Li and Q. Wang, “Local log-euclidean covariance matrix (l2ecm) for image representation and its applications,” In Proc. ECCV, vol. 7574, pp. 469–482, 2012.
  12. P. Rodrguez, B. Wohlberg, “Efficient minimization method for a generalized total variation functional,” IEEE Trans. Image Process., vol. 18, no. 2, pp. 322–332. 2009.
  13. A. Buades, T. M. Le, J.-M. Morel, and L. A. Vese, “Fast cartoon+texture image filters,” IEEE Trans. Image Process., vol. 19, no. 8, pp. 1978–1986, 2010.
Index Terms

Computer Science
Information Sciences

Keywords

Image smoothing structure preserving texture eliminating Log-transform total variation.