Research Article
Log-transform Weighted Total Variation for Image Smoothing
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 124 - Issue 2 |
| Published: August 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 },
year = { 2015 },
volume = { 124 },
number = { 2 },
pages = { 35-40 },
doi = { 10.5120/ijca2015905411 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2015
%A Qiegen Liu
%A Li Zhu
%A Jianhua Wu
%T Log-transform Weighted Total Variation for Image Smoothing%T
%J International Journal of Computer Applications
%V 124
%N 2
%P 35-40
%R 10.5120/ijca2015905411
%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
- L. Xu, Q. Yan, Y. Xia, J. Jia, “Structure extraction from texture via relative total variation,” ACM Trans. Graph., vol. 31, no. 6, 2012.
- 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.
- C. Tomasi, and R. Manduchi, “Bilateral filtering for gray and color images,” In ICCV, pp. 839–846. 1998.
- 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.
- L. Karacan, E. Erdem and A. Erdem, “Structure preserving image smoothing via region covariances,” ACM Trans. Graph., vol. 32, no. 6, Nov. 2013.
- L. Rudin, S. Osher, E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D., vol. 60, pp. 259–68, 1992.
- 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.
- L. Xu, C. Lu, Y. Xu, J. Jia, “Image smoothing via L0 gradient minimization,” ACM Trans Graph., vol. 30, no. 6, 2011.
- R. C. Gonzalez, R. E. Woods, Digital Image Processing. 3rd ed., Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 2006.
- 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.
- 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.
- 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.
- 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
Keywords