Guided Image Filtering

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6311)


In this paper, we propose a novel type of explicit image filter - guided filter. Derived from a local linear model, the guided filter generates the filtering output by considering the content of a guidance image, which can be the input image itself or another different image. The guided filter can perform as an edge-preserving smoothing operator like the popular bilateral filter [1], but has better behavior near the edges. It also has a theoretical connection with the matting Laplacian matrix [2], so is a more generic concept than a smoothing operator and can better utilize the structures in the guidance image. Moreover, the guided filter has a fast and non-approximate linear-time algorithm, whose computational complexity is independent of the filtering kernel size. We demonstrate that the guided filter is both effective and efficient in a great variety of computer vision and computer graphics applications including noise reduction, detail smoothing/enhancement, HDR compression, image matting/feathering, haze removal, and joint upsampling.


Bilateral Filter Local Linear Model Dark Channel Alpha Matte Detail Layer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supplementary material

978-3-642-15549-9_1_MOESM1_ESM.pdf (52 kb)
Electronic Supplementary Material (52 KB)


  1. 1.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: ICCV (1998)Google Scholar
  2. 2.
    Levin, A., Lischinski, D., Weiss, Y.: A closed form solution to natural image matting. In: CVPR (2006)Google Scholar
  3. 3.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice-Hall, Englewood Cliffs (2002)Google Scholar
  4. 4.
    Fattal, R., Lischinski, D., Werman, M.: Gradient domain high dynamic range compression. In: SIGGRAPH (2002)Google Scholar
  5. 5.
    Pérez, P.: Poisson image editing. In: SIGGRAPH (2003)Google Scholar
  6. 6.
    Sun, J., Jia, J., Tang, C.K., Shum, H.Y.: Poisson matting. In: SIGGRAPH (2004)Google Scholar
  7. 7.
    Levin, A., Lischinski, D., Weiss, Y.: Colorization using optimization. In: SIGGRAPH (2004)Google Scholar
  8. 8.
    Farbman, Z., Fattal, R., Lischinski, D., Szeliski, R.: Edge-preserving decompositions for multi-scale tone and detail manipulation. In: SIGGRAPH (2008)Google Scholar
  9. 9.
    He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. In: CVPR (2009)Google Scholar
  10. 10.
    Aurich, V., Weule, J.: Non-linear gaussian filters performing edge preserving diffusion. In: Mustererkennung 1995, DAGM-Symposium, vol. 17, pp. 538–545. Springer, Heidelberg (1995)Google Scholar
  11. 11.
    Petschnigg, G., Agrawala, M., Hoppe, H., Szeliski, R., Cohen, M., Toyama, K.: Digital photography with flash and no-flash image pairs. In: SIGGRAPH (2004)Google Scholar
  12. 12.
    Durand, F., Dorsey, J.: Fast bilateral filtering for the display of high-dynamic-range images. In: SIGGRAPH (2002)Google Scholar
  13. 13.
    Bae, S., Paris, S., Durand, F.: Two-scale tone management for photographic look. In: SIGGRAPH (2006)Google Scholar
  14. 14.
    Paris, S., Durand, F.: A fast approximation of the bilateral filter using a signal processing approach. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 568–580. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Porikli, F.: Constant time o(1) bilateral filtering. In: CVPR (2008)Google Scholar
  16. 16.
    Yang, Q., Tan, K.H., Ahuja, N.: Real-time o(1) bilateral filtering. In: CVPR (2009)Google Scholar
  17. 17.
    Adams, A., Gelfand, N., Dolson, J., Levoy, M.: Gaussian kd-trees for fast high-dimensional filtering. In: SIGGRAPH (2009)Google Scholar
  18. 18.
    Liu, C., Freeman, W.T., Szeliski, R., Kang, S.B.: Noise estimation from a single image. In: CVPR (2006)Google Scholar
  19. 19.
    Fattal, R., Agrawala, M., Rusinkiewicz, S.: Multiscale shape and detail enhancement from multi-light image collections. In: SIGGRAPH (2007)Google Scholar
  20. 20.
    Winnemöller, H., Olsen, S.C., Gooch, B.: Real-time video abstraction. In: SIGGRAPH (2006)Google Scholar
  21. 21.
    Kopf, J., Cohen, M., Lischinski, D., Uyttendaele, M.: Joint bilateral upsampling. In: SIGGRAPH (2007)Google Scholar
  22. 22.
    Yuan, L., Sun, J., Quan, L., Shum, H.Y.: Progressive inter-scale and intra-scale non-blind image deconvolution. In: SIGGRAPH (2008)Google Scholar
  23. 23.
    Weiss, Y.: Segmentation using eigenvectors: A unifying view. In: ICCV (1999)Google Scholar
  24. 24.
    Elad, M.: On the origin of the bilateral filter and ways to improve it. IEEE Transactions on Image Processing (2002)Google Scholar
  25. 25.
    Fattal, R.: Edge-avoiding wavelets and their applications. In: SIGGRAPH (2009)Google Scholar
  26. 26.
    Zomet, A., Peleg, S.: Multi-sensor super resolution. In: IEEE Workshop on Applications of Computer Vision (2002)Google Scholar
  27. 27.
    Draper, N., Smith, H.: Applied Regression Analysis, 2nd edn. John Wiley, Chichester (1981)zbMATHGoogle Scholar
  28. 28.
    Crow, F.: Summed-area tables for texture mapping. In: SIGGRAPH (1984)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Department of Information EngineeringThe Chinese University of Hong Kong 
  2. 2.Microsoft Research Asia 
  3. 3.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesChina

Personalised recommendations