Guided Image Filtering

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 us...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 35; no. 6; pp. 1397 - 1409
Main Authors: He, Kaiming, Sun, Jian, Tang, Xiaoou
Format: Journal Article
Language:English
Published: Los Alamitos, CA IEEE 01.06.2013
IEEE Computer Society
Subjects:
Applied sciences
Artificial intelligence
bilateral filter
Computer science; control theory; systems
Edge-preserving filtering
Exact sciences and technology
Histograms
Image edge detection
Jacobian matrices
Joints
Kernel
Laplace equations
linear time filtering
Pattern recognition. Digital image processing. Computational geometry
Smoothing methods
Computer vision
Data compression
Local theory
Bilateral
Linear time
Image compression
Color image
Linear model
Guidance
Color reproduction
Smoothing
Linear filter
Computer graphics
Linear filtering
Edge-preserving filtering
Image enhancement
linear time filtering
bilateral filter
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:Get full text
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Summary: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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content type line 23
ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2012.213