Guided Image Filtering. He, K., Sun, J., & Tang, X. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(6):1397–1409, June, 2013. Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligencedoi abstract bibtex 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.
@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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