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Reseach Article

Weighted Guided Image Filtering – A Survey

by Nidhi Sen, Akhilesh Jain, Swapnil Jain
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 156 - Number 10
Year of Publication: 2016
Authors: Nidhi Sen, Akhilesh Jain, Swapnil Jain
10.5120/ijca2016912541

Nidhi Sen, Akhilesh Jain, Swapnil Jain . Weighted Guided Image Filtering – A Survey. International Journal of Computer Applications. 156, 10 ( Dec 2016), 29-32. DOI=10.5120/ijca2016912541

@article{ 10.5120/ijca2016912541,
author = { Nidhi Sen, Akhilesh Jain, Swapnil Jain },
title = { Weighted Guided Image Filtering – A Survey },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2016 },
volume = { 156 },
number = { 10 },
month = { Dec },
year = { 2016 },
issn = { 0975-8887 },
pages = { 29-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume156/number10/26746-2016912541/ },
doi = { 10.5120/ijca2016912541 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:02:15.609015+05:30
%A Nidhi Sen
%A Akhilesh Jain
%A Swapnil Jain
%T Weighted Guided Image Filtering – A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 156
%N 10
%P 29-32
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

It is well-known that local filtering-based edge preserving smoothing method suffers from halo artifacts. In this paper, a weighted guided image filter is introduced by incorporating an edge-aware weighting into an accessible guided image filter to address the problem. The WGIF inherit benefits of both global and local smoothing filters in the sense that: 1) the difficulty of the WGIF is O(N) for an image with N pixels, which is same as the GIF and 2) the WGIF can avoid halo artifact like the existing global smoothing filters. The WGIF is applied for single image detail enhancement, single image mist removal, and fusion of differently exposed images. Investigational results show that the resultant algorithms create images with better visual quality and at the same time halo artifacts can be avoided from appearing in the final images with negligible rise on running times.

References
  1. Zhengguo Li, Jinghong Zheng, Zijian Zhu, Wei Yao, Shiqian Wu “Weighted Guided Image Filtering” IEEE Transactions on Image Processing, Vol. 24, No. 1, January 2015 1057-7149 © 2014 IEEE.
  2. B. Y. Zhang and J. P. Allebach, “Adaptive bilateral filter for sharpness enhancement and noise removal,” IEEE Trans. Image Process., vol. 17, no. 5, pp. 664–678, May 2008
  3. A. K. Moorthy and A. C. Bovik, “A two-step framework for constructing blind image quality indices,” IEEE Signal Process. Lett. vol. 17, no. 5, pp. 513–516, May 2010.
  4. J. Pang, O. C. Au, and Z. Guo, “Improved single image dehazing using guided filter,” in Proc. APSIPA ASC, Xi’an, China, 2011, pp. 1–4.
  5. P. Charbonnier, L. Blanc-Feraud, G. Aubert, and M. Barlaud, “Deterministic edge-preserving regularization in computed imaging,” IEEE Trans. Image Process., vol. 6, no. 2, pp. 298–311, Feb. 1997.
  6. Pierre Charbonnier, Laure Blanc-F´eraud, Gilles Aubert, Michel Barlaud “Deterministic Edge-Preserving Regularization in Computed Imaging” IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 6, NO. 2, FEBRUARY 1997
  7. T. Mertens, J. Kautz, and F. Van Reeth, “Exposure fusion: A simple and practical alternative to high dynamic range photography,” Comput. Graph. Forum, vol. 28, no. 1, pp. 161–171, 2009.
  8. K. Moorthy and A. C. Bovik, “A two-step framework for constructing blind image quality indices,” IEEE Signal Process. Lett., vol. 17, no. 5, pp. 513–516, May 2010.
  9. L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D, Nonlinear Phenomena, vol. 60, nos. 1–4, pp. 259–268, Nov. 1992.
  10. Z. G. Li, J. H. Zheng, and S. Rahardja, “Detail-enhanced exposure fusion,” IEEE Trans. Image Process., vol. 21, no. 11, pp. 4672–4676, Nov. 2012.
  11. R. Fattal, M. Agrawala, and S. Rusinkiewicz, “Multiscale shape and detail enhancement from multi-light image collections,” ACM Trans. Graph, vol. 26, no. 3, pp. 51:1–51:10, Aug. 2007.
  12. R. C. Gonzalez and R. E. Woods, Digital Image Processing. Upper Saddle River, NJ, USA: Prentice-Hall, 2002.
  13. Levin, D. Lischinski, and Y. Weiss, “A closed-form solution to natural image matting,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 2, pp. 228–242, Feb. 2008.
  14. J. Preetham, P. Shirley, and B. Smits, “A practical analytic model for daylight,” in Proc. SIGGRAPH, 1999, pp. 91–100.
  15. S. G. Narasimhan and S. K. Nayar, “Chromatic framework for vision in bad weather,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2000, pp. 598–605.
Index Terms

Computer Science
Information Sciences

Keywords

Edge-preserving smoothing weighted guided image filter edge-aware weighting detail enhancement haze removal exposure fusion .