Guided image filtering

  • Authors:
  • Kaiming He;Jian Sun;Xiaoou Tang

  • Affiliations:
  • Department of Information Engineering, The Chinese University of Hong Kong;Microsoft Research Asia;Department of Information Engineering, The Chinese University of Hong Kong and Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China

  • Venue:
  • ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
  • Year:
  • 2010

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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.