Guided Image Filtering
He, Kaiming and Sun, Jian and Tang, Xiaoou

Guided Image Filtering.pdf8.55MB
Type: Paper

author= {He, Kaiming and Sun, Jian and Tang, Xiaoou},
journal= {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
title= {Guided Image Filtering},
year= {2013},
volume= {35},
number= {6},
pages= {1397-1409},
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.},
keywords= {},
doi= {10.1109/TPAMI.2012.213},
issn= {0162-8828},
month= {June},
terms= {}

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