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Guided Image Filtering
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BibTeX
@MISC{He_guidedimage,
author = {Kaiming He and Jian Sun and Xiaoou Tang},
title = {Guided Image Filtering},
year = {}
}
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Abstract
Abstract. In this paper, we propose a novel type of explicit image filter- guided filter. Derived from a local linear model, the guided filter generates 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 perform as an edge-preserving smoothing operator like the popular bilateral filter [1], but has better behavior near the edges. It also has a theoretical connection with the matting Laplacian matrix [2], so is a more generic concept than a smoothing operator and can better utilize the structures in the guidance image. Moreover, the guidedfilterhasafastandnon-approximatelinear-time algorithm, whose computational complexity is independent of the filtering kernel size. We demonstrate that the guided filter is both effective and efficient in a great variety of computer vision and computer graphics applications including noise reduction, detail smoothing/enhancement, HDR compression, image matting/feathering, haze removal, and joint upsampling. 1
Keyphrases
guided filter guidance image different image novel type filtering kernel size popular bilateral filter smoothing operator local linear model computer vision detail smoothing enhancement great variety laplacian matrix filtering output edge-preserving smoothing operator noise reduction haze removal input image joint upsampling computer graphic application computational complexity explicit image filter hdr compression theoretical connection generic concept
