@article{he_guided_2013,
	title = {Guided {Image} {Filtering}},
	volume = {35},
	issn = {1939-3539},
	doi = {10.1109/TPAMI.2012.213},
	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.},
	language = {en},
	number = {6},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	author = {He, Kaiming and Sun, Jian and Tang, Xiaoou},
	month = jun,
	year = {2013},
	note = {Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence},
	keywords = {\#ECCV{\textgreater}10, \#Vision, /unread, Edge-preserving filtering, Guided Image Filtering, Histograms, Image edge detection, Jacobian matrices, Joints, Kernel, Laplace equations, Smoothing methods, bilateral filter, linear time filtering},
	pages = {1397--1409},
}

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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.","language":"en","number":"6","journal":"IEEE Transactions on Pattern Analysis and Machine Intelligence","author":[{"propositions":[],"lastnames":["He"],"firstnames":["Kaiming"],"suffixes":[]},{"propositions":[],"lastnames":["Sun"],"firstnames":["Jian"],"suffixes":[]},{"propositions":[],"lastnames":["Tang"],"firstnames":["Xiaoou"],"suffixes":[]}],"month":"June","year":"2013","note":"Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence","keywords":"#ECCV\\textgreater10, #Vision, /unread, Edge-preserving filtering, Guided Image Filtering, Histograms, Image edge detection, Jacobian matrices, Joints, Kernel, Laplace equations, Smoothing methods, bilateral filter, linear time filtering","pages":"1397–1409","bibtex":"@article{he_guided_2013,\n\ttitle = {Guided {Image} {Filtering}},\n\tvolume = {35},\n\tissn = {1939-3539},\n\tdoi = {10.1109/TPAMI.2012.213},\n\tabstract = {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. 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