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Guided Image Filtering
June 2013 (vol. 35 no. 6)
pp. 1397-1409
Kaiming He, Microsoft Research Asia, Beijing
Jian Sun, Microsoft Research Asia, Beijing
Xiaoou Tang, The Chinese University of Hong Kong, Shatin
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.
Index Terms:
Image edge detection,Kernel,Smoothing methods,Joints,Histograms,Laplace equations,Jacobian matrices,linear time filtering,Edge-preserving filtering,bilateral filter
Kaiming He, Jian Sun, Xiaoou Tang, "Guided Image Filtering," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 6, pp. 1397-1409, June 2013, doi:10.1109/TPAMI.2012.213
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