A generalized relative total variation method for image smoothing
- 387 Downloads
Recently, two piecewise smooth models L0smoothing and relative total variation (RTV) have been proposed for feature/structure-preserving filtering. One is very efficient for tackling image with little texture patterns and the other has appearance performance on image with abundant uniform textural details. In this work, we present a general relative total variation (GRTV) method, which generalizes the advantages of both approaches. The efficiency of RTV depends on the defined windowed total variation (WTV) and windowed inherent variation (WIV), which focus on edge enhancing and texture suppressing respectively. The key innovations of the presented GRTV method are to extend the norm of WTV in RTV from 1 to [0, 1] and set the norm of WIV inversely proportional to the norm of WTV. These modifications substantially improve the structure extraction ability of RTV. The presented GRTV also improves the edge-boundary enhancing ability of L0smoothing and further enables it to deal with images containing complex textural details and noises. Furthermore, the L2-norm data fidelity term replaced by L1-norm is discussed. Experimental results demonstrate that the proposed method presents better performance as the state-of-the-art methods do.
KeywordsImage smoothing Structure preserving non-convex regularization Iterative Reweighed Least Square
This work was partly supported by the National Natural Science Foundation of China under 61362001, 62162084, 61261010, 61365013, 51165033, the Science and Technology Department of Jiangxi Province of China under 20121BBE50023, 20132BAB211030, Young Scientists Training Program of Jiangxi Province under 20133ACB21007, 20142BCB23001, International Scientific Cooperation Project of Jiangxi Province under 20141BDH80001, Jiangxi Advanced Project for Post-doctoral Research Funds under 2014KY02 and Post-doctoral Research Funds under 2014 M551867.
- 11.Fattal R, Agrawala M, Rusinkiewicz S (2007) Multiscale shape and detail enhancement from multi-light image collections. ACM Trans Graph 26:3Google Scholar
- 13.He K, Sun J, Tang X (2010) Guided Image Filtering. In ECCV: 1–14Google Scholar
- 14.Karacan L, Erdem E, Erdem A (2013) Structure preserving image smoothing via region covariances. ACM Trans Graph 32(6)Google Scholar
- 17.Perazzi F et al. (2012) Saliency filters: Contrast based filtering for salient region detection. In CVPRGoogle Scholar
- 20.Rodrguez P, Wohlberg B (2008) An efficient algorithm for sparse representations with lp data fidelity term, in: Proceedings of 4th IEEE Andean Technical Conference (ANDESCON)Google Scholar
- 22.Rouselle F, Knaus C, Zwicker M (2012) Adaptive rendering with non-local means filtering, ACM Trans Graph 31(6).Google Scholar
- 24.Shen C-T, Chang F-J, Hung Y-P, Pei S-C (2010) Edge-preserving image decomposition using L1 fidelity with L0 gradient, SIGGRAPH ASIAGoogle Scholar
- 25.Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In ICCV. 839–846Google Scholar
- 27.Xu L, Lu C, Xu Y, Jia J (2011) Image smoothing via L0 gradient minimization. ACM Trans Graph 30(6);174:1–174:12. doi: 10.1145/2024156.2024208
- 28.Xu L, Yan Q, Xia Y, Jia J (2012) Structure extraction from texture via relative total variation. ACM Trans Graph 31(6)Google Scholar
- 29.Ye C, Tao D, Song M, Jacobs DW (2013) Sparse norm filtering, M Wu - arXiv preprint arXiv:1305.3971, 2013 - arxiv.org.Google Scholar