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      Guided image filtering.

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          Abstract

          In this paper, we propose a novel explicit image filter called guided filter. Derived from a local linear model, the guided filter computes 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 be used as an edge-preserving smoothing operator like the popular bilateral filter [1], but it has better behaviors near edges. The guided filter is also a more generic concept beyond smoothing: It can transfer the structures of the guidance image to the filtering output, enabling new filtering applications like dehazing and guided feathering. Moreover, the guided filter naturally has a fast and nonapproximate linear time algorithm, regardless of the kernel size and the intensity range. Currently, it is one of the fastest edge-preserving filters. Experiments show that the guided filter is both effective and efficient in a great variety of computer vision and computer graphics applications, including edge-aware smoothing, detail enhancement, HDR compression, image matting/feathering, dehazing, joint upsampling, etc.

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          Author and article information

          Journal
          IEEE Trans Pattern Anal Mach Intell
          IEEE transactions on pattern analysis and machine intelligence
          Institute of Electrical and Electronics Engineers (IEEE)
          1939-3539
          0098-5589
          Jun 2013
          : 35
          : 6
          Affiliations
          [1 ] Visual Computing Group, Microsoft Research Asia, Microsoft Building 2, #5 Dan Leng Street, Hai Dian District, Beijing 100080, China. kahe@microsoft.com
          Article
          10.1109/TPAMI.2012.213
          23599054
          b8fc9cfd-f65c-4bfc-87d3-c7fd54919242
          History

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