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Guided Image Filtering

  • Kaiming He
  • Jian Sun
  • Xiaoou Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6311)

Abstract

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.

Keywords

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)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Kaiming He
    • 1
  • Jian Sun
    • 2
  • Xiaoou Tang
    • 1
    • 3
  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

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